Universal microbial diagnostics using random dna probes

ABSTRACT

The present disclosure is directed to compositions and methods present a universal microbial diagnostic (UMD) platform to screen for microbial organisms in a sample using a small number of random DNA probes that are agnostic to the target DNA sequences. The UMD platform can be used to direct and monitor appropriate treatments, thus minimizing the risk of antibiotic resistance, and enhancing patient care.

The present application claims the priority benefit of U.S. provisionalapplication No. 62/480,808, filed Apr. 3, 2017, the entire contents ofwhich is incorporated herein by reference.

GOVERNMENT SUPPORT CLAUSE

This invention was made with government support under grant no.CCF-0728867 awarded by the National Science Foundation, grant no.DGE-0940902 awarded by the National Science Foundation, and grant no.T32EB009379 awarded by the National Institutes of Health. The governmenthas certain rights in the invention.

BACKGROUND 1. Field of the Disclosure

The present disclosure relates generally to the fields of medicine,infectious disease, and microbiology. More particular, the disclosurerelates to compositions and methods for the detection and analysis ofbacterial infections.

2. Background

The accurate, efficient, and rapid identification of microbial organismssuch as bacteria and viruses is of mounting importance in the fields ofhealth care, environmental monitoring, defense, and beyond (Klompas &Yokoe, 2009; Pinto, 2013 and Dorst et al., 2010). Sepsis from bacterialinfection is currently the 11^(th) leading cause of death in the UnitedStates, and the mortality rate of bloodstream infections is high(14-50%) (Martin et al., 2003; Hoyer & Xu, 2012 and Mylotte & Tayara,2000).

Conventional strategies for microbial detection are based onmicrobe-specific genomic or proteomic markers and protocols. Polymerasechain reaction (PCR)-based approaches rely on the binding of specificcapture probes with unique genomic identifiers, such as the 16Sribosomal DNA subunit in bacteria. While these methods show promise ashighly specific tools for microbial identification (Dark et al., 2009and Pechorsky et al., 2009), they have limitations in clinical,industrial, and defense settings (Sontakke et al., 2009 and Sibley etal., 2012). In the case of an epidemic, the detection of a newly mutatedspecies using current PCR methods would require entirely new captureprobes to be manufactured, introducing additional costs and delays. Forbacterial detection, blood cultures typically require 48 to 72 hours toproduce reliable results (Riedel & Carroll, 2010; Paolucci et al., 2010;Bauer & Reinhart, 2010 and Peters et al., 2004). During this waitingperiod, administration of broad-spectrum antibiotics breeds furtherthreats of bacterial resistance and missed coverage (Centers for DiseaseControl and Prevention Antibiotic Resistance threats in the UnitedStates, 2013). DNA microarrays also require many target-specific probesto detect multiple pathogens and lie dormant against unknown organisms.Whole genome sequencing (WGS), currently the most complete and accuratetechnique, is not yet conducive to point-of-care diagnostics; itrequires millions of expensive sequencing reads to assemble or alignwith genomic identifiers. It follows that there is a critical need for anew means of microbial detection: a universal (i.e., works for bacteriaoutside of the target library), inexpensive (i.e., requires minimalresources for acquisition such as DNA probes and sequencing reads,etc.), and rapid sensing platform capable of identifying known and novelspecies with high phylogenetic power.

SUMMARY

Thus, in accordance with the present disclosure, there is provided amethod of detecting a bacterial infection in a subject comprising (a)providing a set of probes comprising SEQ ID NOS: 1, 2, 3, 4 and 5, andoptionally having SEQ ID NOS: 6, 7, 8, 9 and 10, respectively,hybridized thereto; (b) providing a first sample from said subject; (c)obtaining hybridization information for each of probes SEQ ID NOS: 1, 2,3, 4 and 5 with one or more bacterial genomes in said sample; and (d)identifying the presence of one or more bacterial genomes in said samplebased on a predetermined hybridization pattern for said set of probeswith a given bacterial genome. The first sample is a body fluid, such asblood, sputum, tears, saliva, mucous or serum, urine, exudate,transudate, tissue scrapings or feces. The method may further comprisetreating said subject for a bacterial infection. The subject may be ahuman or non-human mammal.

Detection may comprise detecting more than one bacterial genome, such asmultiple bacterial genomes from the same species, or multiple bacterialgenomes are from different species. The multiple different bacterialspecies may be from 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60,70, 80, 90 or 100 different bacterial species. Each of the probes may becomprised within a molecular beacon, such as a molecular beacon thatcomprises a 38 nucleotide loop sequence and a four nucleotide-long stemsequence. The probes may each comprises a label, such as a FRET label,or dual FRET labels. In one embodiment, obtaining hybridizationinformation may comprise measuring FRET from dual labels on said each ofsaid probes. The bacterium or bacterial genomes may be from pathogenicbacteria, from gram-negative bacteria, gram-positive bacteria orgram-indeterminate bacteria. The bacterial genomes may be from a mixtureof gram-indeterminate bacteria with (a) gram-negative bacteria, (b)gram-positive bacteria, or (c) gram-negative and gram-positive bacteria.The bacterium or bacterial genomes may be from Escherichia coli,Pseudomona aeruginosa and/or Staphylococcus aureus.

Step (c) may comprise obtaining quantitative hybridization information,and optionally the method may further comprise quantitating the numberof bacterial genomes in said sample, and optionally may even furthercomprise performing steps (a)-(d) on a second sample from said subject.The second sample may have been obtained at a second point in time ascompared to said first sample, and the number of bacterial genomes insaid first and second samples is compared. The anti-bacterial therapymay have been administered between obtaining of said first and secondsamples, and said method assesses therapeutic efficacy.

In another embodiment, there is provided a method of classifying abacterial infection in a subject comprising (a) providing a set ofprobes comprising SEQ ID NOS: 1, 2, 3, 4 and 5, and optionally havingSEQ ID NOS: 6, 7, 8, 9 and 10, respectively, hybridized thereto; (b)providing a first sample from said subject with a bacterial infection orsuspected to have a bacterial infection; (c) obtaining hybridizationinformation for each of probes SEQ ID NOS: 1, 2, 3, 4 and 5 with one ormore bacterial genomes in said sample; and (d) classifying the bacterialinfection by identifying the presence of one or more bacterial genomesin said sample based on a predetermined hybridization pattern for saidset of probes with a given bacterial genome.

A further embodiment comprise a method of treating a bacterial infectionin a subject comprising (a) providing a set of probes comprising SEQ IDNOS: 1, 2, 3, 4 and 5, and optionally having SEQ ID NOS: 6, 7, 8, 9 and10, respectively, hybridized thereto; (b) providing a first sample fromsaid subject; (c) obtaining hybridization information for each of probesSEQ ID NOS: 1, 2, 3, 4 and 5 with one or more bacterial genomes in saidsample; (d) identifying the presence of one or more bacterial genomes insaid sample based on a predetermined hybridization pattern for said setof probes with a given bacterial genome; and (e) treating said subjectbased on the identification of one or more bacterial genomes in step(d).

An additional embodiment comprises a kit comprising a set of probescomprising SEQ ID NOS: 1, 2, 3, 4 and 5, and optionally having SEQ IDNOS: 6, 7, 8, 9 and 10, respectively, hybridized thereto. Each of saidprobes may be comprised within a molecular beacon, such as a molecularbeacon that comprises a 38 nucleotide loop sequence and a fournucleotide-long stem sequence. One or more or all of said probes maycontain labels, such as Forster Resonance Energy Transfer labels, suchas dual FRET labels. The kit may further comprise bacterial genomes orfragments thereof to serve a positive controls. The kit may furthercomprise one or more buffers, solvents or diluents. The kit may furthercomprise one or more containers for rehydrating a lyophilized reagent.The kit may further comprise one or more positive or negative controlprobes.

In one embodiment, provided herein are methods of selecting a set of Mrandom DNA probes, the method comprising: (a) generating a sensingmatrix comprising the hybridization affinity of D random DNA probes to Nbacterial species; and (b) determining the set of M random DNA probeshaving a smallest average coherence among the bacterial species. In someaspects, the M random DNA probes is a subset of the D random DNA probes.In some aspects, M is smaller than D. In some aspects, D is 100 and N is42. In some aspects, the set of M random DNA probes comprises at leastfifteen probes, wherein an average Basis Pursuit recovery performance indetecting bacterial species in a sample comprising at least threeorganisms is greater than 90%. In some aspects, the set of M random DNAprobes comprises Molecular Beacons. In some aspects, the set of M randomDNA probes comprises toehold probes.

In one embodiment, methods are provided for detecting a bacterialinfection in a subject comprising: (a) providing a set of M random DNAprobes selected according to the method of claim 36; (b) providing afirst sample from said subject; (c) obtaining hybridization informationfor each of the M random DNA probes with one or more bacterial genomesin said sample; and (d) identifying the presence of one or morebacterial genomes in said sample based on said hybridizationinformation. In some aspects, the hybridization information is obtainedby single molecule FISH.

In some aspects, the identifying in step (d) comprises comparing thehybridization information obtained in step (c) with a predeterminedhybridization pattern for the set of M random DNA probes with a givenbacterial genome. In some aspects, the identifying in step (d) comprisesperforming compression sensing on the hybridization information obtainedin step (c).

In some aspects, said first sample is a body fluid. In some aspects,said first sample is blood, sputum, tears, saliva, mucous or serum,urine, exudate, transudate, tissue scrapings or feces. In some aspects,detection comprises detecting more than one bacterial genome. In certainaspects, the more than one bacterial genomes are from the same species.In certain aspects, the more than one bacterial genomes are fromdifferent species. In certain aspects, the more than one differentbacterial species are from 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40,50, 60, 70, 80, 90 or 100 different bacterial species.

In some aspects, each of said probes are comprised within a molecularbeacon. In some aspects, each of said probes are comprised within atoehold probe. In certain aspects, said molecular beacon comprises a 38nucleotide loop sequence and a four nucleotide-long stem sequence. Insome aspects, each of said probes carries a label. In certain aspects,said probe is a Forster Resonance Energy Transfer label. In certainaspects, obtaining hybridization information comprises measuring FRETfrom dual labels on said each of said molecular beacons.

In some aspects, said bacterial genomes are from pathogenic bacteria. Insome aspects, said bacterial genomes are from gram-negative bacteria. Insome aspects, said bacterial genomes are from gram-positive bacteria. Insome aspects, said bacterial genomes are from gram-indeterminatebacteria. In some aspects, said bacterial genomes are from a mixture ofgram-indeterminate bacteria with (a) gram-negative bacteria, (b)gram-positive bacteria, or (c) gram-negative and gram-positive bacteria.In some aspects, said bacterial genomes are from Escherichia coli,Pseudomona aeruginosa and/or Staphylococcus aureus.

In some aspects, step (c) comprises obtaining quantitative hybridizationinformation. In some aspects, the method further comprises quantitatingthe number of bacterial genomes in said sample. In some aspects, themethod further comprises performing steps (a)-(d) on a second samplefrom said subject. In certain aspects, said second sample was obtainedat a second point in time as compared to said first sample, and thenumber of bacterial genomes in said first and second samples iscompared. In certain aspects, an anti-bacterial therapy is administeredbetween obtaining of said first and second samples, and said methodassesses therapeutic efficacy.

In some aspects, the method further comprises treating said subject fora bacterial infection. In some aspects, said subject is a human ornon-human mammal.

In one embodiment, provided herein are kits comprising a set of probescomprising M random DNA probes selected according to a method of thepresent embodiments. In some aspects, each of said probes are comprisedwithin a molecular beacon. In some aspects, each of said probes arecomprised within a toehold probe. In certain aspects, said molecularbeacon comprises a 38 nucleotide loop sequence and a fournucleotide-long stem sequence. In some aspects, one or more of saidprobes contain dual Forster Resonance Energy Transfer labels. In someaspects, the kits further comprise bacterial genomes or fragmentsthereof to serve a positive controls. In some aspects, the kits furthercomprise one or more buffers, solvents or diluents. In some aspects, thekits further comprise one or more containers for rehydrating alyophilized reagent. In some aspects, the kits further comprise one ormore positive or negative control probes.

The use of the word “a” or “an” when used in conjunction with the term“comprising” in the claims and/or the specification may mean “one,” butit is also consistent with the meaning of “one or more,” “at least one,”and “one or more than one.” The word “about” means plus or minus 5% ofthe stated number.

It is contemplated that any method or composition described herein canbe implemented with respect to any other method or composition describedherein. Other objects, features and advantages of the present disclosurewill become apparent from the following detailed description. It shouldbe understood, however, that the detailed description and the specificexamples, while indicating specific embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the disclosure will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The following drawings form part of the present specification and areincluded to further demonstrate certain aspects of the presentdisclosure. The disclosure may be better understood by reference to oneor more of these drawings in combination with the detailed descriptionof specific embodiments presented herein.

FIGS. 1A-C. Schematic of Universal Microbial Diagnostics (UMD) platform.(FIG. 1A) Genomic DNA is extracted from a bacterial sample and thermalcycled with M random DNA probes. The genome probe binding is quantified,producing a probe-binding vector y; in this study the random probes arein the form of molecular beacons (MBs), and the DNA-probe binding isquantified by the ratio of open/hybridized to closed/non-hybridized MBs.(FIG. 1B) The hybridization binding level of each probe to a potentiallylarge reference database of N bacterial genomes (B1, B2, . . . , BN) ispredicted using a thermodynamic model and stored in an M×N hybridizationaffinity matrix Φ. (FIG. 1C) Assuming K bacterial species comprises thesample, the probe-binding vector y is a sparse linear combination of thecorresponding K columns of the matrix Φ weighted by the bacterialconcentrations x, i.e., y=Φx+n, where the vector n accounts for noiseand modeling errors. When K is small enough and M is large enough, Φ canbe effectively inverted using techniques from compressive sensing,yielding the estimate for the microbial makeup of the sample x; in thisillustration the K=2 bacteria labeled B2 and B7 are present in thesample.

FIGS. 2A-C. Random probe design and hybridization affinity computationprocess. (FIG. 2A) DNA sequence structure of five test random DNAprobes. (FIG. 2B) Both strands of the bacterial genome (blue lines) arefirst thermodynamically aligned with the probe sequence. The sequence ofthe bacteria is segmented into fragments of roughly equal length(˜100-200 nts), each containing a significant hybridization affinitywith the probe. Then, all of the bacterial fragments and probe sequencesalong with the experimental conditions are fed into the DNA software(SantaLucia & Hicks, 2004) to predict all stable probe-bacteriacomplexes and concentrations. These concentrations, in aggregate,determine the concentration of opened molecular beacons, which isdefined as the hybridization affinity of the probes to the bacterialgenome. (FIG. 2C) Example of a predicted probe-bacteria fragment bindingwith many base pair mismatches.

FIGS. 3A-E. Binding patterns of five random probes correctly identifiesthe bacteria present in nine diverse bacterial samples. (FIG. 3A)Experimentally measured FRET ratios to quantify hybridization betweenbacterial DNA and probes 1, 2, 3, 4, and 5. (FIG. 3B) Hybridizationaffinity between DNA samples and probes, converted from FRET ratiothrough the probe characteristic curve fit equations (Table 1 and FIG.6). (FIG. 3C) Heat map of normalized inner products between theexperimentally obtained hybridization affinity and predictedhybridization affinities (by thermodynamic model) for nine DNA samplesas a measure of the similarity of the probe measurements to the bacteriain the dataset. DNA samples are clustered into three groups: I. Exactsequence known, II. Exact sequence unknown, and III. Clinical isolates(whose exact sequence is unknown). UMD correctly recovers the diagonallyhighlighted bacterium (with inner product>0.9). (FIG. 3D) The averagereceiver operating characteristic (ROC) curve of UMD in detecting ninebacteria, assuming the independence of the different experiments. Eachpoint on the curve corresponds to a threshold value between [−1, 1]. UMDachieves high values of the area under the curve (AUC>0.9). (FIG. 3E)Correlation of measured and simulated hybridization affinities and thenormalized root-mean-square error (NRMSE) of the prediction (straightline corresponds to maximum correlation). All experiments were performedin triplicate, and the results shown here average over the trials withthe error bars representing the standard error of the mean (s.e.m.).

FIGS. 4A-E. Performance of UMD platform in genus-level recovery of 40species listed as the most common human infectious genera by Centers forDisease Control and Prevention (CDC) with different number of randomprobes M and noise variance σ. (FIG. 4A) The ROC curve in detectingsingle bacterium (K=1) with different noise levels. σ₀=2.4×10⁻⁸ Mdenotes the variance of the additive white Gaussian noise (AWGN) used inthe simulation. This value is obtained from the experiments in FIG. 3Aby calculating the propagated variance of measured FRET ratios. UMDperforms more accurately with lower noise variance. The detection isalmost perfect (AUC>0.95) under noise variance σ=σ₀/5. (FIG. 4B) Theaverage ROC curve in detecting single bacterium using different numberof random probes M and fixed noise variance σ=σ₀. The detectionperformance universally improves over all the 40 species by increasingthe number of random probes. With 15 random probes, UMD achieves almostperfect detection performance (AUC>0.95). (FIG. 4C) The percentage ofsimulated trials, where K bacteria present in the samples were recoveredcorrectly with zero false positives, among all possible

$\begin{pmatrix}40 \\K\end{pmatrix}\quad$

bacteria mixtures (blue and red curves corresponding to K=2 and K=3bacteria, respectively). Simulations were repeated 1000 times withrandomly selected MBs, and error bars represent standard deviation (SD).(FIGS. 4D-E) Confusion matrices illustrating the detection result of UMDusing M=3 and M=10 probes selected by the greedy probe selection (GPS)algorithm.

FIG. 5. Comparison of the sloppiness of random probes to other molecularbeacons. The predicted number of opened probes when the E. coli genomeis exposed to a specific (in the same thermodynamic condition as theexperiment) traditional molecular beacon (MB) (Tyagi & Kramer, 1996),sloppy MB (Chakravorty et al., 2010), and an MB created according torandom design rules. While the nucleotides in the loop were determinedrandomly, the percentage of opened beacons (hybridization affinity to E.coli) is substantially increased for the random probe MB, due to thechoice of stem/loop length.

FIG. 6. Random probes' characteristic curves. Probe characteristiccurves for random probes 1-5 for determining the experimentalrelationship between FRET ratio and open MB concentration (hybridizationaffinity). The characteristic curve is fitted to the measured FRET ratioas a function of the concentration of the probe's exact complement forfive random probes.

FIG. 7. Experimentally measured FRET ratios to quantify hybridizationbetween 11 bacterial DNA samples and probes 1-5. The error barsrepresent the standard error of the mean (s.e.m.).

FIG. 8. Hybridization affinity between 11 bacterial DNA samples andprobes 1-5. Converted from FRET ratio through the probe characteristiccurve fit equations. The error bars represent the standard error of themean (s.e.m.).

FIGS. 9A-B. Detection performance of 11 bacterial samples using fiverandom probes. (FIG. 9A) The heat map and raw values of inner product ofthe experimentally obtained hybridization affinity and predictedhybridization affinity (by thermodynamic model) for eleven DNA samplesas a measure of the similarity of the probe measurements. For detectionof the two strains marked with asterisk (*), only four random probes(Probes 1, 2, 3, and 5) were utilized. (FIG. 9B) The average receiveroperating characteristic (ROC) curve of UMD in detecting elevenbacteria, assuming the independence of different test trials. Each pointon the curve corresponds to a threshold value between [−1, 1]. UMDachieves high values of the area under the curve using experimentallyobtained Φexp (red line) and simulated Φsim using the DNA software.

FIG. 10. Comparison of the predicted concentrations of bacterial DNAwith the experimentally measured values. Concentration of bacterialsamples are estimated by compressive sensing recovery algorithm.

FIGS. 11A-B. Performance of UMD in species-level recovery of 24 strainsof Staphylococcus and 23 strains of Vibrio. (FIG. 11A) UMD's confusionmatrix in identifying 24 strains of Staphylococcus. With M=11 randomprobes, UMD identifies all the species of Staphylococcus genera withAUC>0.95. (FIG. 11B) UMD's confusion matrix in identifying 23 strains ofVibrio. With M=18 random probes, UMD identifies all the species ofVibrio with AUC>0.95.

FIG. 12. Performance of UMD in identifying pathogens in genus-levelusing 15 GPS probes. Using 15 GPS-selected probes, the differencebetween inner product values for various species is increased (whencompared to detection results using M=3 probes in FIG. 4D and M=10probes in FIG. 4E), leading to better robustness against noise and lowerfalse positives (AUC=0.9971).

FIG. 13. Performance of UMD in identifying eight pathogenic and onenonpathogenic E. coli strains using GPS probes. GPS selects 6 UMD probesthat differentiate between eight pathogenic and one nonpathogenic E.coli strains in silico.

FIG. 14. Performance of UMD in identifying the composition of severalcomplex samples. The inventors simulate the number of required randomprobes M to identify the composition of complex bacterial samples with100% accuracy using UMD. The complexity of each sample is measured bythe number of present bacterial species K among a total of N=1500bacterial genera. To obtain each data point in the curve, the inventorsrandomly selected K bacterial genera from the database and created asample by mixing K sample species from each genera with equalconcentrations. They used UMD to recover the composition of the mixtureusing random probes, and repeated this same experiment for 1000 trialsand reported the minimum number of probes that recovered all 1000mixtures accurately. UMD requires orders of magnitudes less number ofrandom probes than the size of the database N (i.e., number of probestypically required by conventional methods) to recover complex samplescontaining mixtures of K species (with equal concentrations) amongN=1500 genera. Furthermore, number of required UMD probes closely(R²=0.98) follows the number of probes predicted by the compressivesensing theory M=ck log(N/K) with c=2.94.

FIG. 15. Schematic of the sensor selection problem for sparse signals.Here, M=3 sensors indexed by Ω={2, 8, 17} are selected from D=20available sensors to recover a K=2-sparse vector x ∈

^(N), N=10, from the linear system y_(Ω)=Φ_(Ω)x.

FIG. 16. Workflow of the Insense algorithm.

FIGS. 17A-F. Comparison of Insense against the baseline algorithms inminimizing the average coherence μ_(avg) and maximum coherence μ_(max)of the selected sensing matrix Φ_(Ω) from random sensing matrices withindependent Gaussian (FIGS. 17A&B), Uniform (FIGS. 17C&D), and Bernoulli(FIGS. 17E&F) entries (D=N=100). Results are averaged over 20 trialswith different random matrices.

FIG. 18. Visualizations of the M=10 sensing matrices Φ_(Ω) selected byInsense and Convex SS from a structured Uniform/Gaussian matrix. Insenseselects 10/10 Gaussian rows (sensors), while Convex SS selects only 4/10Gaussian rows.

FIG. 19. Schematic of magnetic bead functionalization.

FIG. 20. Performance of magnetic bead functionalization. 0.1 nM ofprobe/protector-bead complexes were incubated with 10 fM to 100 nM oftarget for 2 hours in a PolyT buffer, and subsequently analyzed viaqPCR. Dynamic range: 10 pM to 100 nM; Limit of Detection: 10 pM.

FIG. 21. Performance of neutravidin surface functionalization. 0.1 nM ofprobe/protector complexes were conjugated to the plate surface, andincubated with 100 fM to 1 uM of target for 2 hours in a buffercontaining polyT and 1% SDS. Supernatant was subsequently analyzed viaqPCR to determine final probe concentrations. Observed dynamic rangespanned 5 orders of magnitude (100 pM to 1 uM), while the Limit ofDetection was 100 pM.

FIG. 22. Schematic for RNAse H induced cleavage of probe/protectorcomplexes.

FIG. 23. Optimization of RNAse H Cleavage. Two incubation times (2 h and6 h) and enzyme concentrations (1× and 5×) were tested to determineconditions for optimal cleavage of probe/protector complexes. A 2 hincubation with 1× enzyme was found to be most ideal. Conditions wereperformed in triplicate.

FIG. 24. Schematic for ScaI-HF induced cleavage of probe/protectorcomplexes.

FIG. 25. Performance of ScaI-HF Induced Cleavage of Probe/ProtectorComplexes. 0.1 nM of probe/protector and varying target concentrations(10 fM to 10 uM) were incubated with ScaIHF for six hours. Solutionswere subsequently analyzed via qPCR to determine final probeconcentrations. Observed dynamic range spanned 5 orders of magnitude(100 pM to 10 uM), while with an approximate LOD of 100 pM. Conditionswere performed in triplicate.

FIG. 26. Effect of Incubation Time on ScaI-HF Cleavage Efficiency. 0.1nM of Probe-protector and probe-target complexes were incubated with 1×ScaI-HF for 1 hour (green) and 24 hours (blue). A probe alone control isshown in purple. The 24 h condition demonstrated 98% cleavage ofprobe-protector complexes relative to the 88% cleavage observed in the 1h condition. Slight cleavage of probe-target complexes was also observedat 24 hours. All conditions were performed in triplicate.

FIG. 27. Performance of Size Exclusion Via Column Chromotography. 0.1 nMof probe were incubated with varying concentrations (10 pM to 100 nM) ofMRSA bacterial genome, and allowed to incubate for 2 h. Solutions werethen run through a column filtration. Retained solution was analyzed viaqPCR. Size Exclusion Via Column Chromotography exhibited poorsensitivity (approximately 10 nM), as a result of incomplete probeclearance, and clogging of the column filter.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

As discussed above, rapid diagnostics for microbial infections areurgently needed, not simply for the delivery of early and effectivetherapy, but for the avoidance of antibiotic resistance. In thisdisclosure, the inventors report on the design and validation of a newmicrobial diagnostic platform that satisfies the above desiderata. Incommon with microarrays and PCR-based techniques, the inventors'Universal Microbial Diagnostics (UMD) platform exposes a microbialsample (which may contain more than one genus/species) to a collectionof DNA probes. In sharp contrast to conventional methods, however, theprobes are randomly generated (and hence target-agnostic) permutationsof nucleotides (nts) that freely hybridize to different spots and todifferent extents on different bacterial genomes. By measuring thedegree to which the sample hybridizes with the collection of randomprobes, the inventors set up a statistical inverse problem to detect thepresence and estimate the concentrations of the various bacteria in thesample. Using signal recovery techniques from the recently developedtheory of compressive sensing (Donoho, 2006; Baraniuk, 2007), theinventors show that it is possible to stably solve this inverse problemeven when the number of probes is significantly smaller than the size ofthe library of possible bacteria of interest. Due to the randomstructure of the probes, and the variabilities that bacterial organismsexhibit in their genomes, UMD is universal, inexpensive, rapid, andphylogenetically informative (random probes bind to arbitrary spots onthe genome). Moreover, due to the universal nature of its probe design,UMD can classify not only known organisms but also novel mutants withtheir closest known relatives.

Furthermore, the inventor's Incoherent Sensor Selection (Insense)algorithm is used to design the optimal probe sequences by optimizingthe average squared coherence of the columns of the selected sensors(rows) via a computationally efficient relaxation (Aghazadeh et al.,2017, which is incorporated by reference herein in its entirety).Insense provides superior performance than existing state-of-the-artsensor selection algorithms, especially in the real-world problems ofmicrobial diagnostics.

These and other aspects of the disclosure are set out below.

I. UNIVERSAL MICROBIAL DETECTION

In Universal Microbial Detection, or “UMD,” (see FIG. 1A), the genomicDNA of an infectious sample is extracted and exposed to a small number Mof DNA probes, which hybridize to the genomic DNA at various locations;this hybridization is experimentally quantified, producing aprobe-binding (or hybridization) vector y whose entries correspond tothe hybridization binding level of each probe with the microbial sample.

A priori, the hybridization binding level of each probe to a referencedatabase of N bacterial genomes is obtained and stored in an M×Nhybridization affinity matrix (FIG. 1B). The hybridization affinitymatrix can be measured either experimentally in vitro or predictedcomputationally in silico. To speed up the probe design and prove theconcept of UMD, here the inventors predict the affinity matrix using athermodynamic model in silico. To compute the entry φ_(ij) in the matrixΦ, the hybridization binding level of probe i to genome j, the inventorsfirst perform a rapid thermodynamic alignment of the sequence of theprobe to the sequence of the genome using the alignment model describedin (SantaLucia & Hicks, 2004). Next, the inventors extract sequencefragments from the genome sequence, which contain a significanthybridization affinity with the probe sequence. The fragment-probemixture is then fed into a thermodynamics-based hybridization model(SantaLucia & Hicks, 2004). This model predicts all possible stableprobe-bacteria fragment bindings along with their resultingconcentrations for a given set of experimental conditions (FIGS. 2B-C).The overall hybridization affinity φ_(ij) is computed by summing theconcentrations of all predicted and stable probe-fragment bindings for aunit concentration of bacterial genome.

Due to an excess concentration of probes as compared to sample DNA, theprobe-binding vector y can be closely approximated as a linearcombination of the predicted hybridization affinities of the species inthe reference genome database (the columns of the matrix) weighted bytheir concentrations x; i.e., y=Φx+n, where the vector n accounts fornoise and modeling errors (FIG. 1C) (see Example 1).

Two key capabilities of the UMD platform are to (i) detect the presenceand (ii) estimate the concentrations x of a potentially large number Nof reference microbial genomes in an infectious sample given only asmall number M of probe-binding measurements y. Simply inverting thematrix is impossible in this case, since it has many more columns thanrows. Fortunately, it is reasonable to assume that only a small number Kof microbial genomes will be present in a given sample, in which casethe concentration vector x is sparse, with K nonzero and N−K zero (orclose to zero) entries; when K<M, one can hope to invert to estimate theK nonzero concentrations. More rigorously (see the SupplementaryMaterials for details), when the columns of Φ are sufficientlyincoherent (close to orthogonal) and when M=cK log(N/K), where c is asmall constant, the inventors can apply the theory of compressivesensing (Donoho, 2006 and Baraniuk, 2007) to recover the concentrationsx from the measurements y via a sparse optimization of the form

${\min\limits_{x}{x}_{0}},{{{subject}\mspace{14mu} {to}\mspace{14mu} {{y - {\Phi \; x}}}_{2}} < {\sigma.}}$

Here, ∥x∥₀ counts the number of non-zero values in the vector x, and σbounds the energy of the noise vector n. Since M=cK log(N/K) scaleslogarithmically with N, the UMD platform has the potential to identifyand estimate the concentrations of a large number N of potentialmicrobial genomes using only a small number M of measurement probes. Toensure that the columns of Φ are incoherent, the inventors use DNAprobes whose sequences are generated via a random permutation ofnucleotides (FIG. 2A). The inventors have demonstrated that a smallfixed set of randomly selected probes induce sufficiently incoherenthybridization patterns across the columns of Φ and enable us to screenfor a group of pathogenic organisms in vitro (FIG. 3). In addition, theinventors showcased the average performance of several sets of randomlyselected probes in universal pathogenic detection in silico (FIG. 4).

This universal sensing strategy can take on any physical embodiment(e.g., quantitative real-time PCR, DNA microarray, or WGS) for thedetection of any DNA sequence (bacterial, viral, or fungal). To test andvalidate the concept, the inventors recovered pathogenic bacteria usingrandom probes in the form of mismatch-tolerant Sloppy Molecular Beacons(Sloppy MBs) (Chakravorty et al., 2010). In a conventional MB (Tyagi &Kramer, 1996) for bacterial detection, the loop sequence is designed totarget specific regions (e.g., 16S rDNA) within a single bacterium (Darket al., 2009) or multiple bacteria (Chakravorty et al., 2010).

In the MB probes for UMD, the loop sequence is selected as a randomsequence (FIG. 2A, FIG. 5, and Example 1) of length 38 nts, and the 4nt-long stem sequence is consistent across all probes, although otherchoices might be utilized (different design tradeoffs are discussedelsewhere (Sheikh, 2010)). The unusually long loop and short stem enablethe random probes to form hybrids with several base pair mismatchesacross the entire bacterial genome and compensate for the lower signalintensity in the absence of DNA amplification methods such as PCR (FIG.2C).

II. INCOHERENT SENSOR SELECTION (INSENSE)

The accelerating demand for capturing signals at high resolution isdriving acquisition systems to employ an increasingly large number ofsensing units. However, factors like manufacturing costs, physicallimitations, and energy constraints typically define a budget on thetotal number of sensors that can be implemented in a given system. Thisbudget constraint motivates the design of “sensor selection” algorithms(Joshi & Boyd, 2009) that intelligently select a subset of sensors froma pool of available sensors in order to lower the sensing cost with onlya small deterioration in acquisition performance.

The inventors extend the classical sensor selection setup, where Davailable sensors obtain linear measurements of a signal x ∈

^(N) according to y=Φx with each row of Φ ∈

^(D×N) corresponding to one sensor. In this setup, the sensor selectionproblem is one of finding a subset Ω of sensors (i.e., rows of Φ) ofsize |Ω|=M such that the signal x can be recovered from its M linearmeasurements

y_(Ω)=Φ_(Ω)x   (1)

with minimal reconstruction error. Here, Φ_(Ω) ∈

^(M×N) is called the sensing matrix; it contains the rows of Φ indexedby Ω.

The lion's share of current sensor selection algorithms (Joshi & Boyd,2009; Shamaiah et al., 2010; Ranieri et al., 2014) select sensors thatbest recover an arbitrary signal x from M>N measurements. In this case,(1) is overdetermined. Given a subset of sensors Ω, the signal x isrecovered simply by inverting the sensing matrix while computing Φ^(†)_(Ω) _(yΩ) , where Φ_(Ω) ^(†) is the pseudoinverse of Φ_(Ω).

Such approaches do not exploit the fact that many real-world signals are(near) sparse in some basis (Candes & Wakin, 2008). It is now well-knownthat (near) sparse signals can be accurately recovered from a number oflinear measurements M«N using sparse recovery/compressive sensing (CS)techniques (Donoho, 2006; Baraniuk, 2007; Candes, 2006). Conventionalsensor selection algorithms are not designed to exploit low-dimensionalsignal structure. Indeed, they typically fail to select the appropriatesensors for sparse signals in this underdetermined setting (M<N).

The inventors developed a new sensor selection framework that finds theoptimal subset of sensors that best recovers a (near) sparse signal xfrom M<N linear measurements (see FIG. 15). In contrast to theconventional sensor selection setting, here the sensing equation (1) isunderdetermined, and it cannot be simply inverted in closed form.

A key challenge in sensor selection in the underdetermined setting isthat one must replace the cost function that has been so useful in theclassical, overdetermined setting, namely the estimation error∥x−{circumflex over (x)}∥₂ ² (or the covariance of the estimation errorin the presence of noise). In the overdetermined setting, this error canbe obtained in closed form simply by inverting equation (1). In theunderdetermined setting, this error has no closed form expression.Indeed, recovery of a sparse vector x from y_(Ω) requires acomputational scheme (Tibshirani, 1996; Donoho et al., 2009).

Fortunately, the sparse recovery theory tells us that one can reliablyrecover a sufficiently sparse vector x from its linear measurementsy_(Ω) when the columns of the sensing matrix Φ_(Ω) are sufficientlyincoherent (Tropp, 2004; Donoho et al., 2006; Herzet et al., 2013).Define the coherence between the columns ϕ_(i) and ϕ_(j) in the sensingmatrix Φ_(Ω) as

${\mu_{ij}\left( \Phi_{\Omega} \right)} = {\frac{{\langle{\varphi_{i},\varphi_{j}}\rangle}}{{\varphi_{i}}\; {\varphi_{j}}}.}$

If the values of μ_(ij)(Φ_(Ω)) for all pairs of columns (i, j) arebounded by a certain threshold, then sparse recovery algorithms such asBasis Pursuit (BP) (Tropp, 2004; Candes et al., 2006; Gribonval &Vandergheynst, 2006) can recover the sparse signal x exactly. Thistheory suggests a new cost function for sensor selection. To select thesensors Ω that reliably recover a sparse vector, one can minimize theaverage squared coherence:

$\begin{matrix}{{\mu_{avg}^{2}\left( \Phi_{\Omega} \right)} = {\frac{1}{\begin{pmatrix}N \\2\end{pmatrix}}{\sum\limits_{1 \leq i < j \leq N}{{\mu_{ij}^{2}\left( \Phi_{\Omega} \right)}.}}}} & (2)\end{matrix}$

The challenge now becomes formulating an optimization algorithm thatselects the subset of the rows of Φ (the sensors) whose columns have thesmallest average squared coherence.

The inventors are the first to propose and study the sparse-signalsensor selection problem. Below it is demonstrated that the standardcost functions used in overdetermined sensor selection algorithms arenot suitable for the underdetermined case. To solve this problem, theinventors developed a new sensor selection algorithm that optimizes thenew cost function (2); call it the “Incoherent Sensor Selection”(Insense) algorithm. Insense employs an efficient optimization techniqueto find a subset of sensors with smallest average coherence among thecolumns of the selected sensing matrix Φ_(Ω). The optimizationtechnique—projection onto the convex set defined by a scaled-boxedsimplex (SBS) constraint—is of independent interest. The codes for theInsense algorithm are available on the world wide web atgithub.com/amirmohan/Insense.git, which are incorporates be referenceherein in their entirety.

The inventors also demonstrate the superior performance of Insense overconventional sensor selection algorithms using an exhaustive set ofexperimental evaluations that include real-world datasets from microbialdiagnostics and six performance metrics: average mutual coherence,maximum mutual coherence, sparse recovery performance, frame potential,condition number, and running time. For the kinds of redundant,coherent, or structured Φ that are common in real-world applications,Insense finds the best subset of sensors in terms of sparse recoveryperformance by a wide margin. Indeed, in these cases, many conventionalsensor selection algorithms fail completely.

A. Conventional Sensor Selection Algorithms

Existing sensor selection algorithms mainly study the sensor selectionproblem in the overdetermined regime (when M≥N) (Joshi & Boyd, 2009;Shamaiah et al., 2010; Ranieri et al., 2014; Ranieri et al., 2012).

In the overdetermined regime, robust signal recovery can be obtainedusing the solution to the least squares (LS) problem in the sensingmodel (1), which motivates as a cost function the mean squared error(MSE) (Das & Kempe, 2008; Golovin et al., 2010; Das & Kempe, 2011) or aproxy of the MSE (Steinberg & Hunter, 1984; Krause et al., 2008; Wang etal., 2004) of the LS solution.

For instance, Joshi & Boyd (2009) employ a convex optimization-basedalgorithm to minimize the log-volume of the confidence ellipsoid aroundthe LS solution of x. Shamaiah et al. (2010) develop a greedy algorithmthat outperforms the convex approach in terms of MSE. FrameSense(Ranieri et al., 2014) minimize the frame potential (FP) of the selectedmatrix:

$\begin{matrix}{{{{FP}\left( \Phi_{\Omega} \right)} = {\sum\limits_{{\forall{{({i,j})} \in \Omega}},{i < j}}{{\langle{\varphi^{i},\varphi^{j}}\rangle}}^{2}}},} & (3)\end{matrix}$

where ϕ^(i) represents the i^(th) row of Φ. Several additional sensorselection algorithms that assume a non-linear observation model (Ford etal., 1989; Chepuri & Leus, 2015) also operate only in the overdeterminedregime.

B. Connections to Compressive Sensing

The inventor's model for sensor selection has strong connections to, andenables powerful extensions of, the CS problem, in which a (near) sparsesignal is recovered from a small number of randomized linearmeasurements (Donoho, 2006; Baraniuk, 2007; Candes, 2006). First, notethat CS theory typically employs random sensing matrices; for instanceit has been shown that many ensembles of random matrices, includingpartial Fourier, Bernoulli, and Gaussian matrices, result in sensingmatrices with guaranteed sparse recovery (Needell & Vershynin, 2009;Needell & Vershynin, 2010). Recently, there have been efforts to designsensing matrices that outperform random matrices for certain recoverytasks (Amini & Marvasti, 2011; Strohmer & Heath, 2003; Tropp et al.,2005; Elad, 2007; Duarte-Carvajalino & Sapiro, 2009). For instance,Grassmannian matrices (Strohmer & Heath, 2003) attain the smallestpossible mutual coherence and hence can lead to better performance insome applications.

However, many real-world applications do not involve random or Grassmannian sensing matrices; rather the sensing matrix is dictated byphysical constraints that are specific and unique to each application.For example, in the sparse microbial diagnostic problem (Aghazadeh etal., 2016, which is incorporated herein by reference in its entirety),the entries of the sensing matrix Φ are determined by the hybridizationaffinity of random DNA probes to microbial genomes and do notnecessarily follow a random distribution. A key outcome of this work isa new approach to construct practical and realizable sensing matricesusing underdetermined sensor selection (via Insense).

C. Problem Statement

Consider a set of D sensors taking nonadaptive, linear measurements of aK-sparse (i.e., with K non-zero elements) vector x ∈

^(N) following the linear system y=Φx, where Φ ∈

^(D×N) is a given sensing matrix. The aim is to select a subset Ω ofsensors of size |Ω|=M«D, such that the sparse vector x can be recoveredfrom the resulting M<N linear measurements y_(Ω)=Φ_(Ω)x with minimalreconstruction error. Here, Φ_(Ω) contains the rows of Φ indexed by Ω,and y_(Ω) contains the entries of y indexed by Ω. This model for thesensor selection problem can be adapted to more general scenarios. Forexample, if the signal is sparse in a basis Ψ, then simply consider Φ=ΘΨas the new sensing matrix, where Θ is the original sensing matrix.

In order to find a subset Ω of sensors (rows of Φ) that best recovers asparse signal x from y_(Ω) (or find one of the solutions if manysolutions exist), the aim is to select a submatrix Φ_(Ω) ∈

^(M×N) that attains the lowest average squared coherence:

$\begin{matrix}{{{\mu_{avg}^{2}\left( \Phi_{\Omega} \right)} = {\frac{1}{\begin{pmatrix}N \\2\end{pmatrix}}{\sum\limits_{1 \leq i < j \leq N}\frac{{{\langle{\varphi_{i},\varphi_{j}}\rangle}}^{2}}{{{\varphi_{i}}\;}^{2}{\varphi_{j}}^{2}}}}},} & (4)\end{matrix}$

where ϕ_(i) denotes the i^(th) column of Φ_(Ω). The term μ_(avg)averages the off-diagonal entries of Φ_(Ω) ^(T)Φ_(Ω) (indexed by1≤i≤j≤N) after column normalization. Other measures of coherence (e.g.,max coherence

$\left. {{\mu_{\max}\left( \Phi_{\Omega} \right)} = \underset{i < j}{1\; \Omega \; {hX}\; \mu_{ij}}} \right)$

can also be employed by slightly modifying the optimization proceduredeveloped below. The inventors choose to work with average coherence dueto its simplicity and the fact that their experiments show that itsperformance is comparable to max coherence.

Define the diagonal selector matrix Z=diag(z) with z=[z₁, z₂, z₃, . . ., z_(D)]^(T) and z_(i) ∈ {0, 1}, where z_(i)=1 indicates that the i^(th)row (sensor) in Φ is selected and z_(i)=0 otherwise. This enables us toformulate the sensor selection problem as the following optimizationproblem:

$\begin{matrix}{{\underset{z \in {\{{0,1}\}}^{D}}{minimize}{\sum\limits_{1 \leq i < j \leq N}\frac{G_{ij}^{2}}{G_{ii}G_{jj}}}},} & (5) \\{{{s.t.\mspace{14mu} G} = {\Phi^{T}Z\; \Phi}},{{1^{T}z} = M},} & \;\end{matrix}$

where 1 is the all-ones vector. This Boolean optimization problem iscombinatorial, since one needs to search over

$\quad\begin{pmatrix}D \\M\end{pmatrix}$

combinations of sensors to find the optimal set Ω.

To overcome this complexity, the inventors relax the Boolean constrainton z_(i) to the box constraint z_(i) ∈ [0, 1] to arrive at the followingproblem:

$\begin{matrix}{{\underset{z \in {\{{0,1}\}}^{D}}{minimize}{\sum\limits_{1 \leq i \leq j \leq N}\; \frac{G_{ij}^{2}}{G_{ii}G_{jj}}}},{{s.t.\mspace{14mu} G} = {\Phi^{T}Z\; \Phi}},{{1^{T}z} = M},} & (6)\end{matrix}$

which supports an efficient gradient-projection algorithm to find anapproximate solution. This algorithm is developed next.

D. The Insense Algorithm

The steps that Insense takes to solve the problem (6) are outlinedbelow. The objective of (6) is slightly modified to:

$\begin{matrix}{{{f_{\epsilon}(z)} = {{\sum\limits_{1 \leq i \leq j \leq N}\; {\frac{G_{ij}^{2} + \epsilon_{1}}{{G_{ii}G_{jj}} + \epsilon_{2}}\mspace{14mu} {where}\mspace{14mu} G}} = {\Phi^{T}Z\; \Phi}}},} & (7)\end{matrix}$

where the small positive constants ∈₂<∈₁«1 make the objectivewell-defined and bounded over z ∈ [0, 1]^(D).

The objective function (7) is smooth and differentiable but non-convex;the box constraints on z are linear. The objective is minimized usingthe iterative gradient-projection algorithm outlined in Alg. 1 (FIG.16). The gradient ∇z f ∈

^(D) can be computed using the multidimensional chain rule ofderivatives (Petersen & Pedersen, 2008) as:

(∇_(z) f)_(i)=(Φ∇_(G) fΦ ^(T))_(ii) for i=1, . . . , D,

The N×N upper triangular matrix ∇_(G)f is the gradient of f in terms ofthe (auxillary) variable G at the point G=Φ^(T)ZΦ, given by:

$\begin{matrix}{\left( {\bigtriangledown \; \sigma \; f} \right)_{ij} = \left\{ \begin{matrix}{\frac{2\; G_{ij}}{{G_{ii}G_{jj}} + \epsilon_{2}},} & {i < j} \\{{- {\sum\limits_{\forall{ \neq i}}\; \frac{G_{}\left( {G_{i\; }^{2} + \epsilon_{1}} \right)}{\left( {{G_{ii}G_{}} + \epsilon_{2}} \right)^{2}}}},} & {i = j} \\{0,} & {{elsewhere}.}\end{matrix} \right.} & (8)\end{matrix}$

The Insense algorithm (Alg. 1; FIG. 16) proceeds as follows. First, thevariables G and z are initialized. Next, the following update isperformed in iteration k:

z ^(k+1) =P _(SBS)(z ^(k)−γ^(k)∇_(z) f(z ^(k))),   (9)

where P_(SBS) denotes the projection onto the convex set defined by thescaled boxed-simplex (SBS) constraints l^(T)z=M and z ∈ [0, 1]^(D). Forcertain bounded step size rules (e.g., γ^(k)≤1/L, where L is theLipschitz constant of ∇_(z)f), the sequence {z^(k)} generated by (9)converges to a critical point of the nonconvex problem (Attouch et al.,2013; Nesterov, 2007). In this implementation, the inventors use abacktracking line search to determine γ^(k) in each step (Nesterov,2007).

E. The SBS Projection

This section details the inventor's approach to solving the SBSprojection problem:

$\begin{matrix}{{\underset{z}{minimize}\mspace{14mu} \frac{1}{2}{{z - y}}_{2}^{2}},{{{subject}\mspace{14mu} {to}\mspace{14mu} {\sum\limits_{i}\; z_{i}}} = {{{M\mspace{14mu} {and}\mspace{14mu} z_{i}} \in {\left\lbrack {0,1} \right\rbrack \mspace{14mu} {\forall i}}} = 1}},\ldots \mspace{14mu},{D.}} & (10)\end{matrix}$

For M>1, the SBS projection problem is significantly different from the(scaled-)simplex constraint (Σ_(i)z_(i)=M) projection problem that hasbeen studied in the literature (Chen & Ye, 2011; Wang &Carreira-Perpinan, 2013; Condat, 2014), due to the additional boxconstraints z_(i) ∈ [0, 1].

The Lagrangian of the problem (10) can be written as:

${{f\left( {z,\lambda,\alpha,\beta} \right)} = {{\frac{1}{2}{{z - y}}_{2}^{2}} + {\lambda \left( {{\sum\limits_{i}\; z_{i}} - M} \right)} + {\sum\limits_{i}\; {\alpha_{i}\left( {- z_{i}} \right)}} + {\sum\limits_{i}\; {\beta_{i}\left( {z_{i} - 1} \right)}}}},$

where λ, α, β are Lagrange multipliers for the equality and inequalityconstraints, respectively. The Karush-Kuhn-Tucker (KKT) conditions aregiven by:

${{z_{i} - y_{i} + \lambda - \alpha_{i} + \beta_{i}} = 0},{\forall i},{{{\sum\limits_{i}\; z_{i}} - M} = 0},{{\alpha_{i}\left( {- z_{i}} \right)} = 0},{{\beta_{i}\left( {z_{i} - 1} \right)} = 0},\alpha_{i},{\beta_{i} \geq 0},{0 \leq z_{i} \leq 1},{\forall{i.}}$

According to the complimentary slackness condition for the boxconstraint z_(i) ∈ [0, 1], there are the following three cases forx_(i):

z _(i)=0: β_(i)=0, α_(i) =y _(i)+λ>0,   (a)

z _(i)=1: α_(i)=0, β_(i)=1−y _(i)−λ>0,   (b)

z _(i) ∈ [0, 1]: α_(i)=β_(i)=0, z _(i) =y _(i)+λ.   (c)

Therefore, the value of λ holds the key to the proximal problem (10).However, finding λ is not an easy task, since it is not known whichentries of z will fall on the boundary of the box constraint (and areequal to either 0 or 1).

Similarly, the entries in z that are equal to 1 can be found by negatingz and y in (10). Let p=−y and assume that its entries are sorted inascending order; a procedure similar to that above shows that theentries in z that are equal to 1 correspond to the first K₁ entries inp, where K₁ is the largest integer K such that Σ_(i)max(min(p_(i)−p_(k)−1, 0), −1)≥−M.

Knowing which entries in z are equal to 0 and 1, the value of λ can besolved for by working with the entries with values in (0, 1). Using case(c) above and denoting the index set of these entries by ζ, one has:

${\lambda = \frac{M - K_{1} - {\sum\limits_{i \in \zeta}\; y_{i}}}{\zeta }},$

and the solution to (10) is given by z_(i)=max(min(y_(i)+λ, 1), 0).

III. MICROBIAL TARGETS

A. Gram-Positive Bacteria

In some aspects of the present disclosure, the compounds disclosedherein may be used to detect a bacterial infection by a Gram-positivebacteria. Gram-positive bacteria contain a thick peptidoglycan layerwithin the cell wall which prevents the bacteria from releasing thestain when dyed with crystal violet. Without being bound by theory, theGram-positive bacteria are often more susceptible to antibiotics.Generally, Gram-positive bacteria, in addition to the thickpeptidoglycan layer, also comprise a lipid monolayer and containteichoic acids which react with lipids to form lipoteichoic acids thatcan act as a chelating agent. Additionally, in Gram-positive bacteria,the peptidoglycan layer is outer surface of the bacteria. ManyGram-positive bacteria have been known to cause disease including, butare not limited to, Streptococcus, Straphylococcus, Corynebacterium,Enterococcus, Listeria, Bacillus, Clostridium, Rathybacter, Leifsonia,and Clavibacter.

B. Gram-Negative Bacteria

In some aspects of the present disclosure, the compounds disclosedherein may be used to detect a bacterial infection by a Gram-negativebacteria. Gram-negative bacteria do not retain the crystal violet stainafter washing with alcohol. Gram-negative bacteria, on the other hand,have a thin peptidoglycan layer with an outer membrane oflipopolysaccharides and phospholipids as well as a space between thepeptidoglycan and the outer cell membrane called the periplasmic space.Gram-negative bacterial generally do not have teichoic acids orlipoteichoic acids in their outer coating. Generally, Gram-negativebacteria also release some endotoxin and contain prions which act asmolecular transport units for specific compounds. Most bacteria areGram-negative. Some non-limiting examples of Gram-negative bacteriainclude Bordetella, Borrelia, Burcelia, Campylobacteria, Escherichia,Francisella, Haemophilus, Helicobacter, Legionella, Leptospira,Neisseria, Pseudomonas, Rickettsia, Salmonella, Shigella, Treponema,Vibrio, and Yersinia.

C. Gram-Indeterminate Bacteria

In some aspects of the present disclosure, the compounds disclosedherein may be used to detect a bacterial infection by aGram-indeterminate bacteria. Gram-indeterminate bacteria do not fullstain or partially stain when exposed to crystal violet. Without beingbound by theory, a Gram-indeteriminate bacteria may exhibit some of theproperties of the Gram-positive and Gram-negative bacteria. Anon-limiting example of a Gram-indeterminate bacteria includeMycobacterium tuberculosis or Mycobacterium leprae.

D. Staphylococcus Aureus

Staphylococcus aureus is a gram-positive, round-shaped bacterium that isa member of the Firmicutes, and is frequently found in the nose,respiratory tract, and on the skin. It is often positive for catalaseand nitrate reduction and is a facultative anaerobe that can growwithout the need for oxygen. Although S. aureus is not alwayspathogenic, it is a common cause of skin infections such as a skinabscess, respiratory infections such as sinusitis, and food poisoning.Pathogenic strains often promote infections by producing virulencefactors such as potent protein toxins, and the expression ofcell-surface proteins that bind and inactivate antibodies. The emergenceof antibiotic-resistant strains of S. aureus such asmethicillin-resistant S. aureus (MRSA) is a worldwide problem inclinical medicine. Despite much research and development there is noapproved vaccine for S. aureus.

An estimated 20% of the human population are long-term carriers of S.aureus which can be found as part of the normal skin flora, in thenostrils, and as a normal inhabitant of the lower reproductive tract ofwomen S. aureus can cause a range of illnesses, from minor skininfections, such as acne, impetigo, boils, cellulitis, folliculitis,carbuncles, scalded skin syndrome, and abscesses, to life-threateningdiseases such as pneumonia, meningitis, osteomyelitis, endocarditis,toxic shock syndrome, bacteremia, and sepsis. It is still one of thefive most common causes of hospital-acquired infections and is often thecause of wound infections following surgery. Each year, around 500,000patients in hospitals of the United States contract a staphylococcalinfection, chiefly by S. aureus.

S. aureus is a facultative anaerobic, gram-positive coccal (round)bacterium also known as “golden staph” and “oro staphira.” S. aureus isnon-motile and does not form spores. In medical literature, thebacterium is often referred to as S. aureus, Staph aureus, or Staph A.S. aureus appears as staphylococci (grape-like clusters) when viewedthrough a microscope, and has large, round, golden-yellow colonies,often with hemolysis, when grown on blood agar plates. S. aureusreproduces asexually by binary fission. Complete separation of thedaughter cells is mediated by S. aureus autolysin, and in its absence ortargeted inhibition, the daughter cells remain attached to one anotherand appear as clusters.

S. aureus is catalase-positive (meaning it can produce the enzymecatalase). Catalase converts hydrogen peroxide (H₂O₂) to water andoxygen. Catalase-activity tests are sometimes used to distinguishstaphylococci from enterococci and streptococci. Previously, S. aureuswas differentiated from other staphylococci by the coagulase test.However, not all S. aureus strains are coagulase-positive and incorrectspecies identification can impact effective treatment and controlmeasures.

1. Role in Disease

While S. aureus usually acts as a commensal bacterium, asymptomaticallycolonizing about 30% of the human population, it can sometimes causedisease. In particular, S. aureus is one of the most common causes ofbacteremia and infective endocarditis. Additionally, it can causevarious skin and soft tissue infections, particularly when skin ormucosal barriers have been breached.

S. aureus infections can spread through contact with pus from aninfected wound, skin-to-skin contact with an infected person, andcontact with objects used by an infected person such as towels, sheets,clothing, or athletic equipment. Joint replacements put a person atparticular risk of septic arthritis, staphylococcal endocarditis(infection of the heart valves), and pneumonia.

Skin infections are the most common form of S. aureus infection. Thiscan manifest in various ways, including small benign boils,folliculitis, impetigo, cellulitis, and more severe, invasivesoft-tissue infections.

S. aureus is extremely prevalent in persons with atopic dermatitis. Itis mostly found in fertile, active places, including the armpits, hair,and scalp. Large pimples that appear in those areas may exacerbate theinfection if lacerated. This can lead to staphylococcal scalded skinsyndrome, a severe form of which can be seen in neonates.

The presence of S. aureus in persons with atopic dermatitis is not anindication to treat with oral antibiotics, as evidence has not shownthis to give benefit to the patient. The relationship between S. aureusand atopic dermatitis is unclear.

S. aureus is also responsible for food poisoning. It is capable ofgenerating toxins that produce food poisoning in the human body. Itsincubation period lasts one to six hours, with the illness itselflasting anywhere from thirty minutes to three days.

S. aureus is the bacterium that is commonly responsible for all majorbone and joint infections. This manifests in one of three forms:osteomyelitis, septic arthritis and infection from a replacement jointsurgery.

S. aureus is a leading cause of bloodstream infections throughout muchof the industrialized world. Infection is generally associated withbreakages in the skin or mucosal membranes due to surgery, injury, oruse of intravascular devices such as catheters, hemodialysis machines,or injected drugs. Once the bacteria have entered the bloodstream, theycan infect various organs, causing infective endocarditis, septicarthritis, and osteomyelitis. This disease is particularly prevalent andsevere in the very young and very old.

Without antibiotic treatment, S. aureus bacteremia has a case fatalityrate around 80%. With antibiotic treatment, case fatality rates rangefrom 15% to 50% depending on the age and health of the patient, as wellas the antibiotic resistance of the S. aureus strain.

2. Diagnosis

Depending upon the type of infection present, an appropriate specimen isobtained accordingly and sent to the laboratory for definitiveidentification by using biochemical or enzyme-based tests. A Gram stainis first performed to guide the way, which should show typicalgram-positive bacteria, cocci, in clusters. Second, the isolate iscultured on mannitol salt agar, which is a selective medium with 7-9%NaCl that allows S. aureus to grow, producing yellow-colored colonies asa result of mannitol fermentation and subsequent drop in the medium'spH.

Furthermore, for differentiation on the species level, catalase(positive for all Staphylococcus species), coagulase (fibrin clotformation, positive for S. aureus), DNAse (zone of clearance on DNaseagar), lipase (a yellow color and rancid odor smell), and phosphatase (apink color) tests are all done. For staphylococcal food poisoning, phagetyping can be performed to determine whether the staphylococci recoveredfrom the food were the source of infection.

Diagnostic microbiology laboratories and reference laboratories are keyfor identifying outbreaks and new strains of S. aureus. Recent geneticadvances have enabled reliable and rapid techniques for theidentification and characterization of clinical isolates of S. aureus inreal time. These tools support infection control strategies to limitbacterial spread and ensure the appropriate use of antibiotics.Quantitative PCR is increasingly being used to identify outbreaks ofinfection.

When observing the evolvement of S. aureus and its ability to adapt toeach modified antibiotic, two basic methods known as “band-based” or“sequence-based” are employed. Keeping these two methods in mind, othermethods such as multilocus sequence typing (MLST), pulsed-field gelelectrophoresis (PFGE), bacteriophage typing, spa locus typing, andSCCmec typing are often conducted more than others. With these methods,it can be determined where strains of MRSA originated and also wherethey are currently.

With MLST, this technique of typing uses fragments of severalhousekeeping genes known as aroE, glpF, gmk, pta, tip, and yqiL. Thesesequences are then assigned a number which give to a string of severalnumbers that serve as the allelic profile. Although this is a commonmethod, a limitation about this method is the maintenance of themicroarray which detects newly allelic profiles, making it a costly andtime-consuming experiment.

With PFGE, a method which is still very much used dating back to itsfirst success in 1980s, remains capable of helping differentiate MRSAisolates. To accomplish this, the technique uses multiple gelelectrophoresis, along with a voltage gradient to display clearresolutions of molecules. The S. aureus fragments then transition downthe gel, producing specific band patters that are later compared withother isolates in hopes of identifying related strains. Limitations ofthe method include practical difficulties with uniform band patterns andPFGE sensitivity as a whole.

Spa locus typing is also considered a popular technique that uses asingle locus zone in a polymorphic region of S. aureus to distinguishany form of mutations. Although this technique is often inexpensive andless time-consuming, the chance of losing discriminatory power makes ithard to differentiate between MLST CCs exemplifies a crucial limitation.

3. Treatment

The treatment of choice for S. aureus infection is penicillin, thoughnearly all human strains are now resistant to this antimicrobial agent.An antibiotic derived from some Penicillium fungal species, penicillininhibits the formation of peptidoglycan cross-linkages that provide therigidity and strength in a bacterial cell wall. The four-memberedβ-lactam ring of penicillin is bound to enzyme DD-transpeptidase, anenzyme that when functional, cross-links chains of peptidoglycan thatform bacterial cell walls. The binding of β-lactam to DD-transpeptidaseinhibits the enzyme's functionality and it can no longer catalyze theformation of the cross-links. As a result, cell wall formation anddegradation are imbalanced, thus resulting in cell death. In mostcountries, however, penicillin resistance is extremely common, andfirst-line therapy is most commonly a penicillinase-resistant β-lactamantibiotic (for example, oxacillin or flucloxacillin, both of which havethe same mechanism of action as penicillin). Combination therapy withgentamicin may be used to treat serious infections, such asendocarditis, but its use is controversial because of the high risk ofdamage to the kidneys. Honey and propolis produced by the South Americanbee Tetragonisca angustula has also been found to have antibacterialactivity towards S. aureus. The duration of treatment depends on thesite of infection and on severity.

Antibiotic resistance in S. aureus was uncommon when penicillin wasfirst introduced in 1943, but by 1950, 40% of hospital S. aureusisolates were penicillin-resistant; by 1960, this had risen to 80%.Today, MRSA is one of a number of greatly feared strains of S. aureuswhich have become resistant to most β-lactam antibiotics. For thisreason, vancomycin, a glycopeptide antibiotic, is commonly used tocombat MRSA. Vancomycin inhibits the synthesis of peptidoglycan, butunlike β-lactam antibiotics, glycopeptide antibiotics target and bind toamino acids in the cell wall, preventing peptidoglycan cross-linkagesfrom forming. MRSA strains are most often found associated withinstitutions such as hospitals, but are becoming increasingly prevalentin community-acquired infections. A recent study by the TranslationalGenomics Research Institute showed that nearly half (47%) of the meatand poultry in U.S. grocery stores were contaminated with S. aureus,with more than half (52%) of those bacteria resistant to antibiotics.This resistance is commonly caused by the widespread use of antibioticsin the husbandry of livestock, including prevention or treatment of aninfection, as well as promoting growth.

Researchers from ETH Zurich have created the endolysin StaphefektSA.100, which is active against S. aureus, including MRSA. Minor skininfections can be treated with triple antibiotic ointment.

E. E. Coli

Escherichia coli is a gram-negative, facultatively anaerobic,rod-shaped, coliform bacterium of the genus Escherichia that is commonlyfound in the lower intestine of warm-blooded organisms (endotherms).Most E. coli strains are harmless, but some serotypes can cause seriousfood poisoning in their hosts, and are occasionally responsible forproduct recalls due to food contamination. The harmless strains are partof the normal flora of the gut, and can benefit their hosts by producingvitamin K₂, and preventing colonization of the intestine with pathogenicbacteria. E. coli is expelled into the environment within fecal matter.The bacterium grows massively in fresh fecal matter under aerobicconditions for 3 days, but its numbers decline slowly afterwards.

E. coli and other facultative anaerobes constitute about 0.1% of gutflora, and fecal-oral transmission is the major route through whichpathogenic strains of the bacterium cause disease. Cells are able tosurvive outside the body for a limited amount of time, which makes thempotential indicator organisms to test environmental samples for fecalcontamination. A growing body of research, though, has examinedenvironmentally persistent E. coli which can survive for extendedperiods outside of a host.

The bacterium can be grown and cultured easily and inexpensively in alaboratory setting, and has been intensively investigated for over 60years. E. coli is a chemoheterotroph whose chemically defined mediummust include a source of carbon and energy. E. coli is the most widelystudied prokaryotic model organism, and an important species in thefields of biotechnology and microbiology, where it has served as thehost organism for the majority of work with recombinant DNA. Underfavorable conditions, it takes only 20 minutes to reproduce.

E. coli and related bacteria possess the ability to transfer DNA viabacterial conjugation or transduction, which allows genetic material tospread horizontally through an existing population. The process oftransduction, which uses the bacterial virus called a bacteriophage, iswhere the spread of the gene encoding for the Shiga toxin from theShigella bacteria to E. coli helped produce E. coli O157:H7, the Shigatoxin-producing strain of E. coli.

E. coli encompasses an enormous population of bacteria that exhibit avery high degree of both genetic and phenotypic diversity. Genomesequencing of a large number of isolates of E. coli and related bacteriashows that a taxonomic reclassification would be desirable. However,this has not been done, largely due to its medical importance, and E.coli remains one of the most diverse bacterial species: only 20% of thegenes in a typical E. coli genome is shared among all strains. In fact,from the evolutionary point of view, the members of genus Shigella (S.dysenteriae, S. flexneri, S. boydii, and S. sonnei) should be classifiedas E. coli strains, a phenomenon termed taxa in disguise. Similarly,other strains of E. coli (e.g., the K-12 strain commonly used inrecombinant DNA work) are sufficiently different that they would meritreclassification.

1. Serotypes

A common subdivision system of E. coli, but not based on evolutionaryrelatedness, is by serotype, which is based on major surface antigens (Oantigen: part of lipopolysaccharide layer; H: flagellin; K antigen:capsule), e.g., O157:H7). It is, however, common to cite only theserogroup, i.e., the O-antigen. At present, about 190 serogroups areknown. The common laboratory strain has a mutation that prevents theformation of an O-antigen and is thus not typeable.

2. Genome Plasticity and Evolution

Like all lifeforms, new strains of E. coli evolve through the naturalbiological processes of mutation, gene duplication, and horizontal genetransfer; in particular, 18% of the genome of the laboratory strainMG1655 was horizontally acquired since the divergence from Salmonella.E. coli K-12 and E. coli B strains are the most frequently usedvarieties for laboratory purposes. Some strains develop traits that canbe harmful to a host animal. These virulent strains typically cause about of diarrhea that is often self-limiting in healthy adults but isfrequently lethal to children in the developing world. More virulentstrains, such as O157:H7, cause serious illness or death in the elderly,the very young, or the immunocompromised.

The genera Escherichia and Salmonella diverged around 102 million yearsago (credibility interval: 57-176 mya) which coincides with thedivergence of their hosts: the former being found in mammals and thelatter in birds and reptiles. This was followed by a split of anEscherichia ancestor into five species (E. albertii, E. coli, E.fergusonii, E. hermannii, and E. vulneris). The last E. coli ancestorsplit between 20 and 30 million years ago.

E. coli belongs to a group of bacteria informally known as coliformsthat are found in the gastrointestinal tract of warm-blooded animals. E.coli normally colonizes an infant's gastrointestinal tract within 40hours of birth, arriving with food or water or from the individualshandling the child. In the bowel, E. coli adheres to the mucus of thelarge intestine. It is the primary facultative anaerobe of the humangastrointestinal tract. As long as these bacteria do not acquire geneticelements encoding for virulence factors, they remain benign commensals.

3. Role in Disease

Most E. coli strains do not cause disease, but virulent strains cancause gastroenteritis, urinary tract infections, and neonatalmeningitis. It can also be characterized by severe abdominal cramps,diarrhea that typically turns bloody within 24 hours, and sometimesfever. In rarer cases, virulent strains are also responsible for bowelnecrosis (tissue death) and perforation without progressing tohemolytic-uremic syndrome, peritonitis, mastitis, septicemia, andgram-negative pneumonia.

There is one strain, E. coli #0157:H7, that produces the Shiga toxin(classified as a bioterrorism agent). This toxin causes prematuredestruction of the red blood cells, which then clog the body's filteringsystem, the kidneys, causing hemolytic-uremic syndrome (HUS). This inturn causes strokes due to small clots of blood which lodge incapillaries in the brain. This causes the body parts controlled by thisregion of the brain not to work properly. In addition, this straincauses the buildup of fluid (since the kidneys do not work), leading toedema around the lungs and legs and arms. This increase in fluid buildupespecially around the lungs impedes the functioning of the heart,causing an increase in blood pressure.

Uropathogenic E. coli (UPEC) is one of the main causes of urinary tractinfections. It is part of the normal flora in the gut and can beintroduced in many ways. In particular for females, the direction ofwiping after defecation (wiping back to front) can lead to fecalcontamination of the urogenital orifices. Anal intercourse can alsointroduce this bacterium into the male urethra, and in switching fromanal to vaginal intercourse, the male can also introduce UPEC to thefemale urogenital system. For more information, see the databases at theend of the article or UPEC pathogenicity.

In May 2011, one E. coli strain, O104:H4, was the subject of a bacterialoutbreak that began in Germany. Certain strains of E. coli are a majorcause of foodborne illness. The outbreak started when several people inGermany were infected with enterohemorrhagic E. coli (EHEC) bacteria,leading to hemolytic-uremic syndrome (HUS), a medical emergency thatrequires urgent treatment. The outbreak did not only concern Germany,but also 11 other countries, including regions in North America.

4. Treatment

The mainstay of treatment is the assessment of dehydration andreplacement of fluid and electrolytes. Administration of antibiotics hasbeen shown to shorten the course of illness and duration of excretion ofenterotoxigenic E. coli (ETEC) in adults in endemic areas and intraveler's diarrhoea, though the rate of resistance to commonly usedantibiotics is increasing and they are generally not recommended. Theantibiotic used depends upon susceptibility patterns in the particulargeographical region. Currently, the antibiotics of choice arefluoroquinolones or azithromycin, with an emerging role for rifaximin.Oral rifaximin, a semisynthetic rifamycin derivative, is an effectiveand well-tolerated antibacterial for the management of adults withnon-invasive traveller's diarrhoea. Rifaximin was significantly moreeffective than placebo and no less effective than ciprofloxacin inreducing the duration of diarrhea. While rifaximin is effective inpatients with E. coli-predominant traveler's diarrhea, it appearsineffective in patients infected with inflammatory or invasiveenteropathogens.

5. Prevention

ETEC is the type of E. coli that most vaccine development efforts arefocused on. Antibodies against the LT and major CFs of ETEC provideprotection against LT-producing ETEC expressing homologous CFs. Oralinactivated vaccines consisting of toxin antigen and whole cells, i.e.,the licensed recombinant cholera B subunit (rCTB)-WC cholera vaccineDukoral have been developed. There are currently no licensed vaccinesfor ETEC, though several are in various stages of development. Indifferent trials, the rCTB-WC cholera vaccine provided high (85-100%)short-term protection. An oral ETEC vaccine candidate consisting of rCTBand formalin inactivated E. coli bacteria expressing major CFs has beenshown in clinical trials to be safe, immunogenic, and effective againstsevere diarrhea in American travelers but not against ETEC diarrhoea inyoung children in Egypt. A modified ETEC vaccine consisting ofrecombinant E. coli strains over expressing the major CFs and a moreLT-like hybrid toxoid called LCTBA, are undergoing clinical testing.

Other proven prevention methods for E. coli transmission includehandwashing and improved sanitation and drinking water, as transmissionoccurs through fecal contamination of food and water supplies.

Causes and risk factors include working around livestock, consumingunpasteurized dairy product, eating undercooked meat, and drinkingimpure water.

F. Pseudomonas Aeruginosa

Pseudomonas aeruginosa is a common Gram-negative, rod-shaped bacteriumthat can cause disease in plants and animals, including humans. Aspecies of considerable medical importance, P. aeruginosa is a multidrugresistant pathogen recognized for its ubiquity, its intrinsicallyadvanced antibiotic resistance mechanisms, and its association withserious illnesses—especially hospital-acquired infections such asventilator-associated pneumonia and various sepsis syndromes.

The organism is considered opportunistic insofar as serious infectionoften occurs during existing diseases or conditions—most notably cysticfibrosis and traumatic burns. It is also found generally in theimmunocompromised but can infect the immunocompetent as in hot tubfolliculitis. Treatment of P. aeruginosa infections can be difficult dueto its natural resistance to antibiotics. When more advanced antibioticdrug regimens are needed adverse effects may result.

It is citrate, catalase, and oxidase positive. It is found in soil,water, skin flora, and most man-made environments throughout the world.It thrives not only in normal atmospheres, but also in low-oxygenatmospheres, thus has colonized many natural and artificialenvironments. It uses a wide range of organic material for food; inanimals, its versatility enables the organism to infect damaged tissuesor those with reduced immunity. The symptoms of such infections aregeneralized inflammation and sepsis. If such colonizations occur incritical body organs, such as the lungs, the urinary tract, and kidneys,the results can be fatal. Because it thrives on moist surfaces, thisbacterium is also found on and in medical equipment, includingcatheters, causing cross-infections in hospitals and clinics. It isimplicated in hot-tub rash. It is also able to decompose hydrocarbonsand has been used to break down tarballs and oil from oil spills. P.aeruginosa is not extremely virulent in comparison with other majorpathogenic bacterial species—for example Staphylococcus aureus andStreptococcus pyogenes—though P. aeruginosa is capable of extensivecolonization, and can aggregate into enduring biofilms. P. aeruginosadoes not fare especially well under suboptimal atmospheric conditions.

P. aeruginosa is commonly found in the exoskeletons and droppings of thedomestic cockroaches—including the American cockroach and the Germancockroach—which are found to be pervasive in households, as well as inhospital settings. The importance of the American cockroach (and otherpests) as potential reservoirs or vectors of P. aeruginosa continues tobe studied.

1. Pathogenesis

An opportunistic, nosocomial pathogen of immunocompromised individuals,P. aeruginosa typically infects the airway, burns, and wounds, and alsocauses other blood infections. Specific forms of infection includepneumonia, septic shock, skin and soft tissue infection,gastrointestinal tract infections and urinary tract infections. It isthe most common cause of infections of burn injuries and of the outerear (otitis externa), and is the most frequent colonizer of medicaldevices (e.g., catheters). Pseudomonas can be spread by equipment thatgets contaminated and is not properly cleaned or on the hands ofhealthcare workers. Pseudomonas can, in rare circumstances, causecommunity-acquired pneumonias, as well as ventilator-associatedpneumonias, being one of the most common agents isolated in severalstudies. Pyocyanin is a virulence factor of the bacteria and has beenknown to cause death in C. elegans by oxidative stress. However,salicylic acid can inhibit pyocyanin production. One in tenhospital-acquired infections is from Pseudomonas. Cystic fibrosispatients are also predisposed to P. aeruginosa infection of the lungs.P. aeruginosa may also be a common cause of “hot-tub rash” (dermatitis),caused by lack of proper, periodic attention to water quality. Sincethese bacteria like moist environments, such as hot tubs and swimmingpools, they can cause skin rash or swimmer's ear. Pseudomonas is also acommon cause of postoperative infection in radial keratotomy surgerypatients. The organism is also associated with the skin lesion ecthymagangrenosum. P. aeruginosa is frequently associated with osteomyelitisinvolving puncture wounds of the foot, believed to result from directinoculation with P. aeruginosa via the foam padding found in tennisshoes, with diabetic patients at a higher risk.

2. Toxins

P. aeruginosa uses the virulence factor exotoxin A to inactivateeukaryotic elongation factor 2 via ADP-ribosylation in the host cell,much as the diphtheria toxin does. Without elongation factor 2,eukaryotic cells cannot synthesize proteins and necrotise. The releaseof intracellular contents induces an immunologic response inimmunocompetent patients. In addition P. aeruginosa uses an exoenzyme,ExoU, which degrades the plasma membrane of eukaryotic cells, leading tolysis. Increasingly, it is becoming recognized that the iron-acquiringsiderophore, pyoverdine, also functions as a toxin by removing iron frommitochondria, inflicting damage on this organelle.

3. Biofilms and Treatment Resistance

Biofilms of P. aeruginosa can cause chronic opportunistic infections,which are a serious problem for medical care in industrializedsocieties, especially for immunocompromised patients and the elderly.They often cannot be treated effectively with traditional antibiotictherapy. Biofilms seem to protect these bacteria from adverseenvironmental factors. P. aeruginosa can cause nosocomial infections andis considered a model organism for the study of antibiotic-resistantbacteria. Researchers consider it important to learn more about themolecular mechanisms that cause the switch from planktonic growth to abiofilm phenotype and about the role of QS in treatment-resistantbacteria such as P. aeruginosa. This should contribute to betterclinical management of chronically infected patients, and should lead tothe development of new drugs. Many genes and factors affect biofilmformation in P. aeruginosa. One of the main gene operons responsible forthe initiation and maintaining the biofilm is the PSL operon. This15-gene operon is responsible for the cell-cell and cell-surfaceinteractions required for cell communication. It is also responsible forthe sequestering of the extracellular polymeric substance matrix. Thismatrix is composed of nucleic acids, amino acids, carbohydrates, andvarious ions. This matrix is one of the main resistance mechanisms inthe biofilms of P. aeruginosa.

Cyclic di-GMP is a major contributor to biofilm adherent properties.This signaling molecule in high quantities makes superadherent biofilms.When suppressed, the biofilms are less adherent and easier to treat.Polysaccharide synthesis locus (PSL) and cdi-GMP form a negativefeedback loop. PSL stimulates cdi-GMP production, while high cd-GMPturns on the operon and increases activity of the operon.

Recent studies have shown that the dispersed cells from P. aeruginosabiofilms have lower c-di-GMP levels and different physiologies fromthose of planktonic and biofilm cells. Such dispersed cells are found tobe highly virulent against macrophages and C. elegans, but highlysensitive towards iron stress, as compared with planktonic cells.

Recently, scientists have been examining the possible genetic basis forP. aeruginosa resistance to antibiotics such as tobramycin. One locusidentified as being an important genetic determinant of the resistancein this species is ndvB, which encodes periplasmic glucans that mayinteract with antibiotics and cause them to become sequestered into theperiplasm. These results suggest a genetic basis exists behind bacterialantibiotic resistance, rather than the biofilm simply acting as adiffusion barrier to the antibiotic.

4. Traditional Diagnosis

Depending on the nature of infection, an appropriate specimen iscollected and sent to a bacteriology laboratory for identification. Aswith most bacteriological specimens, a Gram stain is performed, whichmay show Gram-negative rods and/or white blood cells. P. aeruginosaproduces colonies with a characteristic “grape-like” or “fresh-tortilla”odor on bacteriological media. In mixed cultures, it can be isolated asclear colonies on MacConkey agar (as it does not ferment lactose) whichwill test positive for oxidase. Confirmatory tests include production ofthe blue-green pigment pyocyanin on cetrimide agar and growth at 42° C.A TSI slant is often used to distinguish nonfermenting Pseudomonasspecies from enteric pathogens in fecal specimens.

When P. aeruginosa is isolated from a normally sterile site (blood,bone, deep collections), it is generally considered dangerous, andalmost always requires treatment. However, P. aeruginosa is frequentlyisolated from nonsterile sites (mouth swabs, sputum, etc.), and, underthese circumstances, it may represent colonization and not infection.The isolation of P. aeruginosa from nonsterile specimens should,therefore, be interpreted cautiously, and the advice of a microbiologistor infectious diseases physician/pharmacist should be sought prior tostarting treatment. Often, no treatment is needed.

5. Identification

P. aeruginosa is a Gram-negative, aerobic (and at times facultativelyanaerobic), bacillus with unipolar motility. It has been identified asan opportunistic pathogen of both humans and plants. P. aeruginosa isthe type species of the genus Pseudomonas. In certain conditions, P.aeruginosa can secrete a variety of pigments, including pyocyanin(blue-green), pyoverdine (yellow-green and fluorescent), and pyorubin(red-brown). These can be used to identify the organism.

P. aeruginosa is often preliminarily identified by its pearlescentappearance and grape-like or tortilla-like odor in vitro. Definitiveclinical identification of P. aeruginosa often includes identifying theproduction of both pyocyanin and fluorescein, as well as its ability togrow at 42° C. P. aeruginosa is capable of growth in diesel and jetfuels, where it is known as a hydrocarbon-using microorganism, causingmicrobial corrosion. It creates dark, gellish mats sometimes improperlycalled “algae” because of their appearance.

6. Treatment

Many P. aeruginosa isolates are resistant to a large range ofantibiotics and may demonstrate additional resistance after unsuccessfultreatment. It should usually be possible to guide treatment according tolaboratory sensitivities, rather than choosing an antibioticempirically. If antibiotics are started empirically, then every effortshould be made to obtain cultures (before administering first dose ofantibiotic), and the choice of antibiotic used should be reviewed whenthe culture results are available.

Due to widespread resistance to many common first-line antibiotics,carbapenems, polymyxins, and more recently tigecycline were consideredto be the drugs of choice; however, resistance to these drugs has alsobeen reported. Despite this, they are still being used in areas whereresistance has not yet been reported. Use of β-lactamase inhibitors suchas sulbactam has been advised in combination with antibiotics to enhanceantimicrobial action even in the presence of a certain level ofresistance. Combination therapy after rigorous antimicrobialsusceptibility testing has been found to be the best course of action inthe treatment of multidrug-resistant P. aeruginosa. Some next-generationantibiotics that are reported as being active against P. aeruginosainclude doripenem, ceftobiprole, and ceftaroline. However, these requiremore clinical trials for standardization. Therefore, research for thediscovery of new antibiotics and drugs against P. aeruginosa is verymuch needed. Antibiotics that may have activity against P. aeruginosainclude aminoglycosides (gentamicin, amikacin, tobramycin, but notkanamycin), quinolones (ciprofloxacin, levofloxacin, but notmoxifloxacin), cephalosporins (ceftazidime, cefepime, cefoperazone,cefpirome, ceftobiprole, but not cefuroxime, cefotaxime, orceftriaxone), antipseudomonal penicillins: carboxypenicillins(carbenicillin and ticarcillin), and ureidopenicillins (mezlocillin,azlocillin, and piperacillin). P. aeruginosa is intrinsically resistantto all other penicillins, carbapenems (meropenem, imipenem, doripenem,but not ertapenem), polymyxins (polymyxin B and colistin) andmonobactams (aztreonam).

As fluoroquinolone is one of the few antibiotics widely effectiveagainst P. aeruginosa, in some hospitals, its use is severely restrictedto avoid the development of resistant strains. On the rare occasionswhere infection is superficial and limited (for example, ear infectionsor nail infections), topical gentamicin or colistin may be used.

7. Antibiotic Resistance

One of the most worrisome characteristics of P. aeruginosa is its lowantibiotic susceptibility, which is attributable to a concerted actionof multidrug efflux pumps with chromosomally encoded antibioticresistance genes (e.g., mexAB, mexXY, etc.) and the low permeability ofthe bacterial cellular envelopes. In addition to this intrinsicresistance, P. aeruginosa easily develops acquired resistance either bymutation in chromosomally encoded genes or by the horizontal genetransfer of antibiotic resistance determinants. Development of multidrugresistance by P. aeruginosa isolates requires several different geneticevents, including acquisition of different mutations and/or horizontaltransfer of antibiotic resistance genes. Hypermutation favours theselection of mutation-driven antibiotic resistance in P. aeruginosastrains producing chronic infections, whereas the clustering of severaldifferent antibiotic resistance genes in integrons favors the concertedacquisition of antibiotic resistance determinants. Some recent studieshave shown phenotypic resistance associated to biofilm formation or tothe emergence of small-colony variants may be important in the responseof P. aeruginosa populations to antibiotics treatment.

Mechanisms underlying antibiotic resistance have been found to includeproduction of antibiotic-degrading or antibiotic-inactivating enzymes,outer membrane proteins to evict the antibiotics and mutations to changeantibiotic targets. Presence of antibiotic-degrading enzymes such asextended-spectrum β-lactamases like PER-1, PER-2, VEB-1, AmpCcephalosporinases, carbapenemases like serine oxacillinases,metallo-b-lactamases, OXA-type carbapenemases, aminoglycoside-modifyingenzymes, among others have been reported. P. aeruginosa can also modifythe targets of antibiotic action, for example methylation of 16S rRNA toprevent aminoglycoside binding and modification of DNA, or topoisomeraseto protect it from the action of quinolones. P. aeruginosa has also beenreported to possess multidrug efflux pumps like AdeABC and AdeDE effluxsystems that confer resistance against number of antibiotic classes. Animportant factor found to be associated with antibiotic resistance isthe decrease in the virulence capabilities of the resistant strain. Suchfindings have been reported in the case of rifampicin-resistant andcolistin-resistant strains, in which decrease in infective ability,quorum sensing and motility have been documented.

Mutations in DNA gyrase are commonly associated with antibioticresistance in P. aeruginosa. These mutations, when combined with others,confer high resistance without hindering survival. Additionally, genesinvolved in cyclic-di-GMP signaling may contribute to resistance. Whengrown in vitro conditions designed to mimic a cystic fibrosis patient'slungs, these genes mutate repeatedly.

8. Prevention

Probiotic prophylaxis may prevent colonization and delay onset ofPseudomonas infection in an ICU setting. Immunoprophylaxis againstPseudomonas is being investigated. The risk of contracting P. aeruginosacan be reduced by avoiding pools, hot tubs, and other bodies of standingwater; regularly disinfecting and/or replacing equipment that regularlyencounters moisture (such as contact lens equipment and solutions); andwashing one's hands often (which is protective against many otherpathogens as well). However, even the best hygiene practices cannottotally protect an individual against P. aeruginosa, given how common P.aeruginosa is in the environment.

G. Other Bacteria

1. Acinetobacter Baumannii

Acinetobacter baumannii is a Gram-negative bacterial pathogen that hasrapidly emerged as a leading cause of infection world-wide. In fact, A.baumannii is now responsible for up to 20% of all intensive care unitinfections in some regions of the world. This organism causes a range ofdiseases, with pneumonia being the most prevalent. As a result of itsresistance to drug treatment, some estimates state the disease iskilling tens of thousands of U.S. hospital patients each year.

A. baumannii forms opportunistic infections. There have been manyreports of A. baumannii infections among American soldiers wounded inIraq, earning it the nickname “Iraqibacter.” Multi-drug resistant A.baumannii is abbreviated as MDRAB. MDRAB is not a new phenomenon; it hasalways been inherently resistant to multiple antibiotics.

A. baumannii is the most relevant human pathogen within theAcinetobacter genus. Most A. baumannii isolates are multiresistant,containing in their genome small, isolated islands of alien (meaningtransmitted genetically from other organisms) DNA and other cytologicaland genetic material; this has led to more virulence. Acinetobacter haveno flagellum; the name is Greek for “motionless.”

Acinetobacter enters into the body through open wounds, catheters, andbreathing tubes. It usually infects those with compromised immunesystems, such as the wounded, the elderly, children or those with immunediseases. Colonization poses no threat to people who aren't already ill,but colonized health care workers and hospital visitors can carry thebacteria into neighboring wards and other medical facilities. The numberof nosocomial infections (hospital-acquired infections) caused by A.baumannii has increased in recent years; as have most other nosocomialpathogens (MRSA, VRSA, VRE, etc.).

The first military outbreaks of severe A. baumannii infections occurredin April, 2003 in American soldiers returning from Iraq. Early reportsattributed the infections to the Iraqi soil. Later testing demonstratedwidespread contamination of field hospitals, via transportation ofpersonnel and equipment from previously contaminated European hospitals,as the most plausible vector.

Nosocomial A. baumannii bacteremia may cause severe clinical diseasethat is associated with an elevated mortality rate. This opportunisticpathogen expresses a myriad of factors that could play a role in humanpathogenesis. Among these factors are the attachment to and persistenceon solid surfaces, the acquisition of essential nutrients such as iron,the adhesion to epithelial cells and their subsequent killing byapoptosis, and the production and/or secretion of enzymes and toxicproducts that damage host tissues. However, very little is known aboutthe molecular nature of most of these processes and factors and almostnothing has been shown with regard to their role in bacterial virulenceand the pathogenesis of serious infectious diseases. Fortunately, someof these gaps can now be filled by testing appropriate isogenicderivatives in relevant animal models that mimic the infections inhumans, particularly the outcome of deadly pneumonia. Such an approachshould provide new and relevant information on the virulence traits ofthis normally underestimated bacterial human pathogen.

Multidrug-resistant A. baumannii is a common problem in many hospitalsin the U.S. and Europe. First line treatment is with a carbapenemantibiotic such as imipenem, but carbapenem resistance is increasinglycommon. Other treatment options include polymyxins, tigecycline andaminoglycosides. The institution of strict infection-control measures,such as monitored hand washing, can lower hospital infection rates.MDRAB infections are difficult and costly to treat. A study at a publicteaching hospital found that the mean total hospital cost of patientswho acquired MDRAB was $98,575 higher than that of control patients whohad identical burn severity of illness indices.

2. Acinetobacter Spp.

Acinetobacter spp. other than A. baumannii include A. calcoaceticus, A.lwoffii, A. junii, A. anitratus, A. baumannii-calcoaciticus complex.Acinetobacter is a Gram-negative genus of bacteria belonging to theGammaproteobacteria. Non-motile, Acinetobacter species areoxidase-negative, and occur in pairs under magnification. They areimportant soil organisms where they contribute to the mineralization of,for example, aromatic compounds. Acinetobacter are a key source ofinfection in debilitated patients in the hospital. Different species ofbacteria in this genus can be identified usingFluorescence-Lactose-Denitrification medium (FLN) to find the amount ofacid produced by metabolism of glucose.

Species of the genus Acinetobacter are strictly aerobic,nonfermentative, Gram-negative bacilli. They show preponderantly acoccobacillary morphology on nonselective agar. Rods predominate influid media, especially during early growth. The morphology ofAcinetobacter spp. can be quite variable in Gram stained human clinicalspecimens, and cannot be used to differentiate Acinetobacter from othercommon causes of infection.

Most strains of Acinetobacter, except some of the A. lwoffii strains,grow well on MacConkey agar (without salt). Although officiallyclassified as non-lactose fermenting, they are often partially lactosefermenting when grown on MacConkey agar. They are oxidase negative,nonmotile and usually nitrate negative.

Acinetobacter species are generally considered nonpathogenic to healthyindividuals. However, several species persist in hospital environmentsand cause severe, life-threatening infections in compromised patients.The spectrum of antibiotic resistances of these organisms together withtheir survival capabilities make them a threat to hospitals asdocumented by recurring outbreaks both in highly developed countries andelsewhere. An important factor for their pathogenic potential isprobably an efficient means of horizontal gene transfer even though sucha mechanism has so far only been observed and analyzed in A. baylyi, aspecies that lives in the soil and has never been associated withinfections. Acinetobacter is frequently isolated in nosocomialinfections and is especially prevalent in intensive care units, whereboth sporadic cases as well as epidemic and endemic occurrence iscommon. A. lwoffi is responsible for most cases of Acinetobactermeningitis.

Acinetobacter species are innately resistant to many classes ofantibiotics, including penicillin, chloramphenicol, and oftenaminoglycosides. Resistance to fluoroquinolones has been reported duringtherapy and this has also resulted in increased resistance to other drugclasses mediated through active drug efflux. A dramatic increase inantibiotic resistance in Acinetobacter strains has been reported by theCDC and the carbapenems are recognized as the gold-standard andtreatment of last resort. Acinetobacter species are unusual in that theyare sensitive to sulbactam; sulbactam is most commonly used to inhibitbacterial beta-lactamase, but this is an example of the antibacterialproperty of sulbactam itself.

3. Burkholderia Spp.

Burkholderia spp. (B. cepacia, B. cenocepacia, B. cepacia complex) aremembers of a genus of proteobacteria probably best-known for itspathogenic members B. mallei (responsible for glanders, a disease thatoccurs mostly in horses and related animals), B. pseudomallei (causativeagent of melioidosis), and B. cepacia (an important pathogen ofpulmonary infections in people with cystic fibrosis).

The Burkholderia (previously part of Pseudomonas) genus name refers to agroup of virtually ubiquitous gram-negative, motile, obligately aerobicrod-shaped bacteria including both animal/human and plant pathogens aswell as some environmentally-important species. In particular, B.xenovorans (previously named Pseudomonas cepacia then B. cepacia and B.fungorum) is renowned for its ability to degrade chlororganic pesticidesand polychlorinated biphenyls (PCBs). Due to their antibiotic resistanceand the high mortality rate from their associated diseases, B. malleiand B. pseudomallei are considered to be potential biological warfareagents, targeting livestock and humans.

4. Klebsiella Pneumoniae

Klebsiella pneumoniae is a Gram-negative, non-motile, encapsulated,lactose fermenting, facultative anaerobic, rod shaped bacterium found inthe normal flora of the mouth, skin, and intestines. It is clinicallythe most important member of the Klebsiella genus of Enterobacteriaceae;it is closely related to K. oxytoca from which it is distinguished bybeing indole-negative and by its ability to grow on both melezitose and3-hydroxybutyrate. It naturally occurs in the soil and about 30% ofstrains can fix nitrogen in anaerobic conditions. As a free-livingdiazotroph, its nitrogen fixation system has been much studied.

Members of the Klebsiella genus typically express 2 types of antigens ontheir cell surface. The first, O antigen, is a lipopolysaccharide ofwhich 9 varieties exist. The second is K antigen, a capsularpolysaccharide with more than 80 varieties. Both contribute topathogenicity and form the basis for subtyping.

Research has implicated molecular mimicry between HLA-B27 and twomolecules in Klebsiella microbes as the cause of ankylosing spondylitis.As a general rule, Klebsiella infections tend to occur in people with aweakened immune system from improper diet (alcoholics and diabetics).Many of these infections are obtained when a person is in the hospitalfor some other reason (a nosocomial infection). The most commoninfection caused by Klebsiella bacteria outside the hospital ispneumonia.

New antibiotic resistant strains of K. pneumoniae are appearing, and itis increasingly found as a nosocomial infection. Klebsiella ranks secondto E. coli for urinary tract infections in older persons. It is also anopportunistic pathogen for patients with chronic pulmonary disease,enteric pathogenicity, nasal mucosa atrophy, and rhinoscleroma. Fecesare the most significant source of patient infection, followed bycontact with contaminated instruments.

Multiply-resistant K. pneumoniae have been killed in vivo viaintraperitoneal, intravenous or intranasal administration of phages inlaboratory tests.

5. Stenotrophomonas Maltophilia

Stenotrophomonas maltophilia is an aerobic, nonfermentative,Gram-negative bacterium. It is an uncommon bacteria and it is difficultto treat infections in humans. Initially classified as Pseudomonasmaltophilia, S. maltophilia was also grouped in the genus Xanthomonasbefore eventually becoming the type species of the genusStenotrophomonas in 1993.

S. maltophilia are slightly smaller (0.7-1.8×0.4-0.7 micrometers) thanother members of the genus. They are motile due to polar flagella andgrow well on MacConkey agar producing pigmented colonies. S. maltophiliaare catalase-positive, oxidase-negative (which distinguishes them frommost other members of the genus) and have a positive reaction forextracellular DNase.

S. maltophilia is ubiquitous in aqueous environments, soil and plants,including water, urine, or respiratory secretions; it has also been usedin biotechnology applications. In immunocompromised patients, S.maltophilia can lead to nosocomial infections.

S. maltophilia frequently colonizes breathing tubes such as endotrachealor tracheostomy tubes, the respiratory tract and indwelling urinarycatheters. Infection is usually facilitated by the presence ofprosthetic material (plastic or metal), and the most effective treatmentis removal of the prosthetic material (usually a central venous catheteror similar device). The growth of S. maltophilia in microbiologicalcultures of respiratory or urinary specimens is therefore sometimesdifficult to interpret and not a proof of infection. If, however, it isgrown from sites which would be normally sterile (e.g., blood), then itusually represents true infection.

In immunocompetent individuals, S. maltophilia is a relatively unusualcause of pneumonia, urinary tract infection, or blood stream infection;in immunocompromised patients, however, S. maltophilia is a growingsource of latent pulmonary infections. S. maltophilia colonization ratesin individuals with cystic fibrosis have been increasing.

S. maltophilia is naturally resistant to many broad-spectrum antibiotics(including all carbapenems) and is thus often difficult to eradicate.Many strains of S. maltophilia are sensitive to co-trimoxazole andticarcillin, though resistance has been increasing. It is not usuallysensitive to piperacillin, and sensitivity to ceftazidime is variable.

6. Haemophilus Influenzae

Haemophilus influenzae, formerly called Pfeiffer's bacillus or Bacillusinfluenzae, is a non-motile Gram-negative rod-shaped bacterium firstdescribed in 1892 during an influenza pandemic. A member of thePasteurellaceae family, it is generally aerobic, but can grow as afacultative anaerobe. H. influenzae was mistakenly considered to be thecause of influenza until 1933, when the viral etiology of the flu becameapparent. Still, H. influenzae is responsible for a wide range ofclinical diseases.

In 1930, 2 major categories of H. influenzae were defined: theunencapsulated strains and the encapsulated strains. Encapsulatedstrains were classified on the basis of their distinct capsularantigens. There are six generally recognized types of encapsulated H.influenzae: a, b, c, d, e, and f. Genetic diversity among unencapsulatedstrains is greater than within the encapsulated group. Unencapsulatedstrains are termed nontypable (NTHi) because they lack capsularserotypes, however they can be classified by multi-locus sequencetyping. The pathogenesis of H. influenzae infections is not completelyunderstood, although the presence of the capsule in encapsulated type b(Hib), a serotype causing conditions such as epiglottitis, is known tobe a major factor in virulence. Their capsule allows them to resistphagocytosis and complement-mediated lysis in the non-immune host. Theunencapsulated strains are almost always less invasive, however they canproduce an inflammatory response in humans which can lead to manysymptoms. Vaccination with Hib conjugate vaccine is effective inpreventing Hib infection. Several vaccines are now available for routineuse against Hib, however vaccines are not yet available against NTHi.

Most strains of H. influenzae are opportunistic pathogens—that is, theyusually live in their host without causing disease, but cause problemsonly when other factors (such as a viral infection or reduced immunefunction) create an opportunity.

Naturally-acquired disease caused by H. influenzae seems to occur inhumans only. In infants and young children, H. influenzae type b (Hib)causes bacteremia, pneumonia, and acute bacterial meningitis.Occasionally, it causes cellulitis, osteomyelitis, epiglottitis, andinfectious arthritis. Due to routine use of the Hib conjugate vaccine inthe U.S. since 1990, the incidence of invasive Hib disease has decreasedto 1.3/100,000 in children. However, Hib remains a major cause of lowerrespiratory tract infections in infants and children in developingcountries where vaccine is not widely used. Unencapsulated H. influenzaecauses ear infections (otitis media), eye infections (conjunctivitis),and sinusitis in children and is associated with pneumonia.

Clinical diagnosis of H. influenzae is typically performed by bacterialculture or latex particle agglutination. Diagnosis is consideredconfirmed when the organism is isolated from a sterile body site. Inthis respect, H. influenzae cultured from the nasopharyngeal cavity orsputum would not indicate H. influenzae disease because these sites arecolonized in disease free individuals. However, H. influenzae isolatedfrom cerebrospinal fluid or blood would indicate a H. influenzaeinfection.

Bacterial culture of H. influenzae is performed on agar plates,preferably Chocolate agar, plate with added X(Hemin) & V(NAD) factors at37° C. in an enriched CO2 incubator. Blood agar growth is only achievedas a satellite phenomenon around other bacteria. Colonies of H.influenzae appear as convex, smooth, pale, grey or transparent colonies.Gram-stained and microscopic observation of a specimen of H. influenzaewill show Gram-negative, coccobacilli, with no specific arrangement. Thecultured organism can be further characterized using catalase andoxidase tests, both of which should be positive. Further serological isnecessary to distinguish the capsular polysaccharide and differentiatebetween H. influenzae b and non-encapsulated species.

Although highly specific, bacterial culture of H. influenzae lacks insensitivity. Use of antibiotics prior to sample collection greatlyreduces the isolation rate by killing the bacteria before identificationis possible. Beyond this, H. influenzae is a finicky bacterium toculture, and any modification of culture procedures can greatly reduceisolation rates. Poor quality of laboratories in developing countrieshas resulted in poor isolation rates of H. influenzae.

H. influenzae will grow in the hemolytic zone of Staphylococcus aureuson Blood Agar plates. The hemolysis of cells by S. aureus releasesnutrients vital to the growth of H. influenzae. H. influenzae will notgrow outside the hemolytic zone of S. aureus due to the lack ofnutrients in these areas.

H. influenzae produces beta lactamases, and it is also able to modifyits penicillin binding protein, so it has gained resistance to thepenicillin family of antibiotics. In severe cases cefotaxime andceftriaxone are the elected antibiotics, delivered directly into thebloodstream, and for the less severe cases an association of ampicillinand sulbactam, cephalosporins of the second and third generation, orfluoroquinolones.

7. Streptococcus Pneumoniae

Streptococcus pneumoniae is a gram-positive, alpha-hemolytic, bilesoluble aerotolerant anaerobe and a member of the genus Streptococcus. Asignificant human pathogenic bacterium, S. pneumoniae was recognized asa major cause of pneumonia in the late 19th century and is the subjectof many humoral immunity studies.

Despite the name, the organism causes many types of pneumococcalinfection other than pneumonia, including acute sinusitis, otitis media,meningitis, bacteremia, sepsis, osteomyelitis, septic arthritis,endocarditis, peritonitis, pericarditis, cellulitis, and brain abscess.S. pneumoniae is the most common cause of bacterial meningitis in adultsand children, and is one of the top two isolates found in ear infection,otitis media. Pneumococcal pneumonia is more common in the very youngand the very old.

S. pneumoniae can be differentiated from S. viridans, some of which arealso alpha hemolytic, using an optochin test, as S. pneumoniae isoptochin sensitive. S. pneumoniae can also be distinguished based on itssensitivity to lysis by bile. The encapsulated, gram-positive coccoidbacteria have a distinctive morphology on gram stain, the so-called,“lancet shape.” It has a polysaccharide capsule that acts as a virulencefactor for the organism; more than 90 different serotypes are known, andthese types differ in virulence, prevalence, and extent of drugresistance.

S. pneumoniae is part of the normal upper respiratory tract flora but aswith many natural flora, it can become pathogenic under the rightconditions (e.g., if the immune system of the host is suppressed).Invasins such as Pneumolysin, an anti-phagocytic capsule, variousadhesins and immunogenic cell wall components are all major virulencefactors.

Both H. influenzae and S. pneumoniae can be found in the human upperrespiratory system. A study of competition in a laboratory revealedthat, in a petri dish, S. pneumoniae always overpowered H. influenzae byattacking it with hydrogen peroxide. When both bacteria are placedtogether into a nasal cavity, within 2 weeks, only S. pneumoniaesurvives. When both are placed separately into a nasal cavity, each onesurvives. Upon examining the upper respiratory tissue from mice exposedto both bacteria, an extraordinarily large number of neutrophil immunecells were found. In mice exposed to only one bacteria, the cells werenot present. Lab tests show that neutrophils that were exposed toalready dead H. influenzae were more aggressive in attacking S.pneumoniae than unexposed neutrophils. Exposure to killed H. influenzaehad no effect on live H. influenzae.

8. Vibrio Cholerae

Vibrio cholera, also known as Kommabacillus, is a gram negativecomma-shaped bacterium with a polar flagellum that causes cholera inhumans. There are two major biotypes of Vibrio cholerae identified byhemaggluttination testing, classical and El Tor, and numerousserogroups. The classical biotype is found only in Bangladesh, whereasthe El Tor is found throughout the world.

Vibrio cholerae pathogenicity genes code for proteins directly orindirectly involved in the virulence of the bacteria. Because of theirsame transcriptional regulation and their implication in the samepathway, pathogenicity genes are generally organized in operons and/orgene clusters. In Vibrio cholerae, most of virulence genes are locatedin two pathogenicity plasmids, which are organized as prophages: CTX(Cholera ToXins) plasmid and TCP (Toxin-Coregulated Pilus) plasmid, alsonamed as Vibrio cholerae Pathogenicity Island (VPI). Virulent andepidemic strains of Vibrio cholerae require these two genetic elementsto cause infections.

9. Vibrio Parahaemolyticus

Vibrio parahaemolyticus is a curved, rod-shaped, Gram-negative bacteriumfound in brackish saltwater that causes gastrointestinal illness inhumans, when ingested. V. parahaemolyticus is oxidase positive,facultatively aerobic, and does not form spores. Like other members ofthe genus Vibrio, this species is motile, with a single, polarflagellum.

While infection of V. parahaemolyticus can occur via the fecal-oralroute, the predominant cause of the acute gastroenteritis caused by V.parahaemolyticus is through ingestion of bacteria in raw or undercookedseafood, usually oysters. Wound infections also occur, but are lesscommon than seafood-borne disease. The disease mechanism of V.parahaemolyticus infections has not been fully elucidated.

Outbreaks tend to be concentrated along coastal regions during thesummer and early fall when higher water temperatures favor higher levelsof bacteria. Seafood most often implicated includes squid, mackerel,tuna, sardines, crab, shrimp, and bivalves like oysters and clams. Theincubation period of ˜24 hours is followed by explosive, watery diarrheaaccompanied by nausea, vomiting, abdominal cramps, and sometimes fever.V. parahaemolyticus symptoms typically resolve with-in 72 hours, but canpersist for up to 10 days in immunocompromised individuals. As the vastmajority of cases of V. parahaemolyticus food infection areself-limiting, treatment is not typically necessary. In severe cases,fluid and electrolyte replacement is indicated.

Additionally, swimming or working in affected areas can lead toinfections of the eyes or ears and open cuts and wounds. FollowingHurricane Katrina, there were three wound infections caused by V.parahaemolyticus and two of these led to death.

10. Yersinia Pseudotuberculosis

Yersinia pseudotuberculosis is a Gram-negative bacterium which primarilycauses Pseudotuberculosis (Yersinia) disease in animals; humansoccasionally get infected zoonotically, most often through thefood-borne route.

In animals, Y. pseudotuberculosis can cause tuberculosis-like symptoms,including localized tissue necrosis and granulomas in the spleen, liver,and lymph node.

In humans, symptoms of Pseudotuberculosis (Yersinia) include fever andright-sided abdominal pain, but the diarrheal component is often absent,which sometimes makes the resulting condition difficult to diagnose. Y.pseudotuberculosis infections can mimic appendicitis, especially inchildren and younger adults, and, in rare cases the disease may causeskin complaints (erythema nodosum), joint stiffness and pain (reactivearthritis), or spread of bacteria to the blood (bacteremia).

Pseudotuberculosis (Yersinia) usually becomes apparent 5-10 days afterexposure and typically lasts 1-3 weeks without treatment. In complexcases or those involving immunocompromised patients, antibiotics may benecessary for treatment; ampicillin, aminoglycosides, tetracycline,chloramphenicol, or a cephalosporin may all be effective.

The recently described syndrome Izumi-fever has also been linked toinfection with Y. pseudotuberculosis.

This bacterium possesses many virulence factors to facilitateattachment, invasion, and colonization of its host. Superantigens,bacterial adhesions, and the actions of Yops (which are bacterialproteins once thought to be “Yersinia outer membrane proteins”) that areencoded on the “[plasmid] for Yersinia virulence”—commonly known as thepYV—cause host pathogenesis and allow the bacteria to liveparasitically.

Y. pseudotuberculosis adheres strongly to intestinal cells viachromosomally encoded proteins so that Yop secretion may occur, to avoidbeing removed by peristalsis, and to invade target host cells.

Certain strains of Y. pseudotuberculosis express a superantigenicexotoxin, YPM, or the Y. pseudotuberculosis-derived mitogen, from thechromosomal ypm gene. Strains which carry the exotoxin gene are rare inWestern countries where the disease, when at all apparent, manifestsitself largely with minor symptoms, whereas more than 95% of strainsfrom Far Eastern countries contain ypm and are correlated with Izumifever and Kawasaki disease.

Although the superantigen poses the greatest threat to host health, allvirulence factors contribute to Y. pseudotuberculosis viability in vivoand define the bacterium's pathogenic characteristics. Y.pseudotuberculosis can live extracellularly due to its formidablemechanisms of phagocytosis and opsonisation resistance; yet, by limitedpYV action, it can populate host cells, especially macrophages,intracellularly to further evade immune responses and be disseminatedthroughout the body.

11. Salmonella

Salmonella is a genus of rod-shaped, Gram-negative, non-spore-forming,predominantly motile enterobacteria with flagella which grade in alldirections (i.e. peritrichous). They are chemoorganotrophs, obtainingtheir energy from oxidation and reduction reactions using organicsources, and are facultative anaerobes. Most species produce hydrogensulfide, which can readily be detected by growing them on mediacontaining ferrous sulfate, such as TSI. Most isolates exist in twophases: a motile phase I and a nonmotile phase II.

Salmonella is closely related to the Escherichia genus and are foundworldwide in cold- and warm-blooded animals, including humans, and inthe environment. They cause illnesses like typhoid fever, paratyphoidfever, and the foodborne illness. Salmonella infections are zoonoticandcan be transferred between humans and nonhuman animals. Manyinfections are due to ingestion of contaminated food.Typhoid/paratyphoid Salmonella is distinguished from enteritisSalmonella because of the possession of a special virulence factor and acapsule protein (virulence antigen), which can cause serious illness,such as Salmonella enterica subsp. enterica serovar Typhi. Salmonellatyphi. is adapted to humans and does not occur in animals.

Enteritis salmonelliosis or Food Poisoning Salmonella is a groupconsisting of potentially all other serotypes (over a thousand) of theSalmonella bacterium, most of which have never been found in humans.These are encountered in various Salmonella species, most having neverbeen linked to a specific host, and can also infect humans. The organismenters through the digestive tract and must be ingested in large numbersto cause disease in healthy adults. Gastric acidity is responsible forthe destruction of the majority of ingested bacteria. The infectionusually occurs as a result of massive ingestion of foods in which thebacteria are highly concentrated similarly to a culture medium. However,infants and young children are much more susceptible to infection,easily achieved by ingesting a small number of bacteria. It has beenshown that, in infants, the contamination could be through inhalation ofbacteria-laden dust.

After a short incubation period of a few hours to one day, the germmultiplies in the intestinal lumen causing an intestinal inflammationwith diarrhea that is often muco-purulent and bloody. In infants,dehydration can cause a state of severe toxicosis. The symptoms areusually mild. There is normally no sepsis, but it can occurexceptionally as a complication in weakened elderly patients (Hodgkin'sdisease, for example). Extraintestinal localizations are possible,especially Salmonella meningitis in children, osteitis, etc. EnteritisSalmonella, e.g., Salmonella enterica subsp. enterica serovarenteritidis, can cause diarrhea, which usually does not requireantibiotic treatment. However, in people at risk such as infants, smallchildren, the elderly, Salmonella infections can become very serious,leading to complications. If these are not treated, HIV patients andthose with suppressed immunity can become seriously ill. Children withsickle cell anaemia who are infected with Salmonella may developosteomyelitis.

In Germany, Salmonella infections must be reported. Between 1990 and2005, the number of officially recorded cases decreased fromapproximately 200,000 cases to approximately 50,000. It is estimatedthat every fifth person in Germany is a carrier of Salmonella. In theUSA, there are approximately 40,000 cases of Salmonella infectionreported each year. According to the World Health Organization, over 16million people worldwide are infected with typhoid fever each year, with500,000 to 600,000 fatal cases.

Salmonella can survive for weeks outside a living body. They have beenfound in dried excrement after more than 2.5 years. Salmonella are notdestroyed by freezing. Ultraviolet radiation and heat accelerate theirdemise; they perish after being heated to 55° C. (131° F.) for one hour,or to 60° C. (140° F.) for half an hour. To protect against Salmonellainfection, it is recommended that food be heated for at least tenminutes at 75° C. (167° F.) so that the center of the food reaches thistemperature.

F. Antibiotics

The term “antibiotics” are drugs which may be used to treat a bacterialinfection through either inhibiting the growth of bacteria or killingbacteria. Without being bound by theory, it is believed that antibioticscan be classified into two major classes: bactericidal agents that killbacteria or bacteriostatic agents that slow down or prevent the growthof bacteria.

The first commercially available antibiotic was released in the 1930's.Since then, many different antibiotics have been developed and widelyprescribed. In 2010, on average, 4 in 5 Americans are prescribedantibiotics annually. Given the prevalence of antibiotics, bacteria havestarted to develop resistance to specific antibiotics and antibioticmechanisms. Without being bound by theory, the use of antibiotics incombination with another antibiotic may modulate resistance and enhancethe efficacy of one or both agents.

In some embodiments, antibiotics can fall into a wide range of classes.In some embodiments, the compounds of the present disclosure may be usedin conjunction with another antibiotic. In some embodiments, thecompounds may be used in conjunction with a narrow spectrum antibioticwhich targets a specific bacteria type. In some non-limiting examples ofbactericidal antibiotics include penicillin, cephalosporin, polymyxin,rifamycin, lipiarmycin, quinolones, and sulfonamides. In somenon-limiting examples of bacteriostatic antibiotics include macrolides,lincosamides, or tetracyclines. In some embodiments, the antibiotic isan aminoglycoside such as kanamycin and streptomycin, an ansamycin suchas rifaximin and geldanamycin, a carbacephem such as loracarbef, acarbapenem such as ertapenem, imipenem, a cephalosporin such ascephalexin, cefixime, cefepime, and ceftobiprole, a glycopeptide such asvancomycin or teicoplanin, a lincosamide such as lincomycin andclindamycin, a lipopeptide such as daptomycin, a macrolide such asclarithromycin, spiramycin, azithromycin, and telithromycin, amonobactam such as aztreonam, a nitrofuran such as furazolidone andnitrofurantoin, an oxazolidonones such as linezolid, a penicillin suchas amoxicillin, azlocillin, flucloxacillin, and penicillin G, anantibiotic polypeptide such as bacitracin, polymyxin B, and colistin, aquinolone such as ciprofloxacin, levofloxacin, and gatifloxacin, asulfonamide such as silver sulfadiazine, mefenide, sulfadimethoxine, orsulfasalazine, or a tetracycline such as demeclocycline, doxycycline,minocycline, oxytetracycline, or tetracycline. In some embodiments, thecompounds could be combined with a drug which acts against mycobacteriasuch as cycloserine, capreomycin, ethionamide, rifampicin, rifabutin,rifapentine, and streptomycin. Other antibiotics that are contemplatedfor combination therapies may include arsphenamine, chloramphenicol,fosfomycin, fusidic acid, metronidazole, mupirocin, platensimycin,quinupristin, dalfopristin, thiamphenicol, tigecycline, tinidazole, ortrimethoprim.

IV. DETECTION MATERIALS AND TECHNIQUES

A. Probes and Primers

Detection of nucleic acids may involve the use of a hybridizationreaction, such as between a target nucleic acid and an oligonucleotideprobe or primer (e.g., a nucleic acid hybridization assay). In someembodiments, the oligonucleotide probe is immobilized on a substrate.Substrates include, but are not limited to, arrays, microarrays, wellsof a multi-well plate, and beads (e.g. non-magnetic, magnetic,paramagnetic, hydrophobic, and hydrophilic beads). Examples of materialsuseful as substrates include but are not limited to nitrocellulose,glass, silicon, and a variety of gene arrays. A preferred hybridizationassay is conducted on high-density gene chips as described in U.S. Pat.No. 5,445,934.

The terms “polynucleotide,” “nucleotide,” “nucleotide sequence,”“nucleic acid,” and “oligonucleotide” are used interchangeably. Theyrefer to a polymeric form of nucleotides of any length, eitherdeoxyribonucleotides or ribonucleotides, or analogs thereof.Polynucleotides may have any three-dimensional structure, and mayperform any function, known or unknown. The following are non-limitingexamples of polynucleotides: coding or non-coding regions of a gene orgene fragment, loci (locus) defined from linkage analysis, exons,introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, ribozymes,cDNA, recombinant polynucleotides, branched polynucleotides, plasmids,vectors, isolated DNA of any sequence, isolated RNA of any sequence,nucleic acid probes, and primers. A polynucleotide may comprise modifiednucleotides, such as methylated nucleotides and nucleotide analogs. Ifpresent, modifications to the nucleotide structure may be impartedbefore or after assembly of the polymer. The sequence of nucleotides maybe interrupted by non-nucleotide components. A polynucleotide may befurther modified after polymerization, such as by conjugation with alabeling component.

A “nucleotide probe” or “probe” refers to a polynucleotide used fordetecting or identifying its corresponding target polynucleotide in ahybridization reaction.

“Hybridization” refers to a reaction in which one or morepolynucleotides react to form a complex that is stabilized via hydrogenbonding between the bases of the nucleotide residues. The hydrogenbonding may occur by Watson-Crick base pairing, Hoogstein binding, or inany other sequence-specific manner. The complex may comprise two strandsforming a duplex structure, three or more strands forming amulti-stranded complex, a single self-hybridizing strand, or anycombination of these. A hybridization reaction may constitute a step ina more extensive process, such as the initiation of a PCR, or theenzymatic cleavage of a polynucleotide by a ribozyme.

The term “hybridized” as applied to a polynucleotide refers to theability of the polynucleotide to form a complex that is stabilized viahydrogen bonding between the bases of the nucleotide residues. Thehydrogen bonding may occur by Watson-Crick base pairing, Hoogsteinbinding, or in any other sequence-specific manner. The complex maycomprise two strands forming a duplex structure, three or more strandsforming a multi-stranded complex, a single self-hybridizing strand, orany combination of these. The hybridization reaction may constitute astep in a more extensive process, such as the initiation of a PCRreaction, or the enzymatic cleavage of a polynucleotide by a ribozyme.

Hybridized nucleic acids may be detected by detecting one or more labelsattached to the sample nucleic acids. The labels can be incorporated byany of a number of means well known to those of skill in the art.

B. Molecular Beacons

Molecular beacons are oligonucleotide hybridization probes that canreport the presence of specific nucleic acids in homogenous solutions.The term more often used is molecular beacon probes. Molecular beaconsare hairpin shaped molecules with an internally quenched fluorophorewhose fluorescence is restored when they bind to a target nucleic acidsequence. This is a novel non-radioactive method for detecting specificsequences of nucleic acids. They are useful in situations where it iseither not possible or desirable to isolate the probe-target hybridsfrom an excess of the hybridization probes.

A typical molecular beacon probe is 25 nucleotides long. The middle 15nucleotides are complementary to the target DNA or RNA and do not basepair with one another, while the five nucleotides at each terminus arecomplementary to each other rather than to the target DNA. A typicalmolecular beacon structure can be divided in 4 parts: 1) loop, an 18-30base pair region of the molecular beacon that is complementary to thetarget sequence; 2) stem formed by the attachment to both termini of theloop of two short (5 to 7 nucleotide residues) oligonucleotides that arecomplementary to each other; 3) 5′ fluorophore at the 5′ end of themolecular beacon, a fluorescent dye is covalently attached; 4) 3′quencher (non-fluorescent) dye that is covalently attached to the 3′ endof the molecular beacon. When the beacon is in closed loop shape, thequencher resides in proximity to the fluorophore, which results inquenching the fluorescent emission of the latter.

If the nucleic acid to be detected is complementary to the strand in theloop, the event of hybridization occurs. The duplex formed between thenucleic acid and the loop is more stable than that of the stem becausethe former duplex involves more base pairs. This causes the separationof the stem and hence of the fluorophore and the quencher. Once thefluorophore is distanced from the quencher, illumination of the hybridwith light results in the fluorescent emission. The presence of theemission reports that the event of hybridization has occurred and hencethe target nucleic acid sequence is present in the test sample.

Molecular beacons are synthetic oligonucleotides whose preparation iswell documented. In addition to the conventional set of nucleosidephosphoramidites, the synthesis also requires a solid supportderivatized with a quencher and a phosphoramidite building blockdesigned for the attachment of a protected fluorescent dye.

C. FRET

Förster resonance energy transfer (FRET), fluorescence resonance energytransfer (FRET), resonance energy transfer (RET) or electronic energytransfer (EET) is a mechanism describing energy transfer between twolight-sensitive molecules (chromophores). A donor chromophore, initiallyin its electronic excited state, may transfer energy to an acceptorchromophore through nonradiative dipole-dipole coupling. The efficiencyof this energy transfer is inversely proportional to the sixth power ofthe distance between donor and acceptor, making FRET extremely sensitiveto small changes in distance.

Measurements of FRET efficiency can be used to determine if twofluorophores are within a certain distance of each other. Suchmeasurements are used as a research tool in fields including biology andchemistry.

FRET is analogous to near-field communication, in that the radius ofinteraction is much smaller than the wavelength of light emitted. In thenear-field region, the excited chromophore emits a virtual photon thatis instantly absorbed by a receiving chromophore. These virtual photonsare undetectable, since their existence violates the conservation ofenergy and momentum, and hence FRET is known as a radiationlessmechanism. Quantum electrodynamical calculations have been used todetermine that radiationless (FRET) and radiative energy transfer arethe short- and long-range asymptotes of a single unified mechanism.

When both chromophores are fluorescent, the term “fluorescence resonanceenergy transfer” is often used instead, although the energy is notactually transferred by fluorescence. In order to avoid an erroneousinterpretation of the phenomenon that is always a non-radiative transferof energy (even when occurring between two fluorescent chromophores),the name “Förster resonance energy transfer” is preferred to“fluorescence resonance energy transfer”; however, the latter enjoyscommon usage in scientific literature. It should also be noted that FRETis not restricted to fluorescence. It can occur in connection withphosphorescence as well.

One common pair fluorophores for biological use is a cyan fluorescentprotein (CFP)-yellow fluorescent protein (YFP) pair. Both are colorvariants of green fluorescent protein (GFP). Labeling with organicfluorescent dyes requires purification, chemical modification, andintracellular injection of a host protein. GFP variants can be attachedto a host protein by genetic engineering which can be more convenient.Additionally, a fusion of CFP and YFP linked by a protease cleavagesequence can be used as a cleavage assay.

A limitation of FRET is the requirement for external illumination toinitiate the fluorescence transfer, which can lead to background noisein the results from direct excitation of the acceptor or tophotobleaching. To avoid this drawback, Bioluminescence Resonance EnergyTransfer (or BRET) has been developed. This technique uses abioluminescent luciferase (typically the luciferase from Renillareniformis) rather than CFP to produce an initial photon emissioncompatible with YFP. One drawback of BRET is the requirement to generateat least one fusion protein encoding luciferase, though someapplications of FRET can be implemented with antibody-conjugatedfluorophores.

BRET has also been implemented using a different luciferase enzyme,engineered from a deep-sea shrimp Oplophorus gracilirostris. Thisluciferase is smaller (19 kD) and brighter than the more commonly usedluciferase from Renilla reniformis. Promega has developed thisluciferase variant under the product name NanoLuc.

In general, “FRET” refers to situations where the donor and acceptorproteins (or “fluorophores”) are of two different types. In manybiological situations, however, researchers might need to examine theinteractions between two, or more, proteins of the same type—or indeedthe same protein with itself, for example if the protein folds or formspart of a polymer chain of proteins or for other questions ofquantification in biological cells.

Obviously, spectral differences will not be the tool used to detect andmeasure FRET, as both the acceptor and donor protein emit light with thesame wavelengths. Yet researchers can detect differences in thepolarisation between the light which excites the fluorophores and thelight which is emitted, in a technique called FRET anisotropy imaging;the level of quantified anisotropy (difference in polarisation betweenthe excitation and emission beams) then becomes an indicative guide tohow many FRET events have happened.

FRET has been used to measure distance and detect molecular interactionsin a number of systems and has applications in biology and chemistry.FRET can be used to measure distances between domains in a singleprotein and therefore to provide information about protein conformation.FRET can also detect interaction between proteins. Applied in vivo, FREThas been used to detect the location and interactions of genes andcellular structures including intergrins and membrane proteins.

The applications of fluorescence resonance energy transfer (FRET) haveexpanded tremendously in the last 25 years, and the technique has becomea staple technique in many biological and biophysical fields. FRET canbe used as spectroscopic ruler in various areas such as structuralelucidation of biological molecules and their interactions in vitroassays, in vivo monitoring in cellular research, nucleic acid analysis,signal transduction, light harvesting and metallic nanomaterial etc.Based on the mechanism of FRET a variety of novel chemical sensors andbiosensors have been developed.

D. Kits

In still further embodiments, the present disclosure concerns kits foruse with the detection methods described above. The detection kits willthus comprise, in suitable container means, one or more probes orprimers that hybridize to target sequences, and optionally controldetection reagents.

In certain embodiments, the probes or primers may be pre-bound to asolid support, such as a filter, a column matrix and/or well of amicrotiter plate. The detection reagents of the kit may take any one ofa variety of forms, including those detectable labels that areassociated with or linked to the probe or primer.

The kits may further comprise a suitably aliquoted composition of atarget sequence, as may be used to prepare a standard for a detectionassay. The kits may contain primer/probe-label conjugates either infully conjugated form, in the form of intermediates, or as separatemoieties to be conjugated by the user of the kit. The components of thekits may be packaged either in aqueous media or in lyophilized form.

The container means of the kits will generally include at least onevial, test tube, flask, bottle, syringe or other container means, intowhich the probes or primers may be placed, or preferably, suitablyaliquoted. The kits of the present disclosure will also typicallyinclude a means for containing the reagent containers in closeconfinement for commercial sale. Such containers may include injectionor blow-molded plastic containers into which the desired vials areretained.

V. EXAMPLES

The following examples are included to demonstrate preferredembodiments. It should be appreciated by those of skill in the art thatthe techniques disclosed in the examples that follow representtechniques discovered by the inventors to function well in the practiceof embodiments, and thus can be considered to constitute preferred modesfor its practice. However, those of skill in the art should, in light ofthe present disclosure, appreciate that many changes can be made in thespecific embodiments which are disclosed and still obtain a like orsimilar result without departing from the spirit and scope of thedisclosure.

Example 1 Materials and Methods for UMD Using MBs

Random DNA probe design. In the design of molecular beacons (MB s)(Tyagi & Kramer, 1996) for random DNA probes, the length and GC content(ratio of G+C to other nucleotides) of the probe loop and stem sequenceswere considered to strike a balance between two factors: fluorescencesignal level and probe stability. Signal intensity was especiallyimportant in this detection scheme, since no DNA amplification method(such as PCR) was utilized. Similar to sloppy molecular beacons (sloppyMBs) (Chakravorty et al., 2010), the inventors selected the random probeloop sequence to be longer that traditional MBs. In addition, theinventors made the stem sequence one nucleotide shorter to introduceadditional sloppiness (i.e., hybridization in presence of more base-pairmismatches).

The challenge was to find probes that maintain the MB's signaturehairpin structure over a wide range of temperatures (4-50° C.) afterintroducing additional sloppiness. To produce random MBs the inventorsfollowed the following procedure: The inventors first generated onemillion random sequences of length 46 nucleotides with fixed stemsequences on both ends (FIG. 2A). Then, the inventors used a package inthe DNA software (Visual OMP DE) to generate all the possible stable andsecondary structures of the sequences in the experimental thermodynamicconditions. The inventors parsed the output of the DNA software andfiltered out the probes with undesired secondary structures or meltingtemperatures. FIG. 5 shows the gain in hybridization affinity obtainedusing the random MBs in comparison with traditional MBs and sloppy MBsin binding to the E. coli genome. By no means is this the only method togenerate random probes for a UMD platform; any method that producesprobes with a stable hairpin structure and uniform melting temperaturewhile providing the required signal intensity can be utilized.

Random DNA probe construction and preparation. To implement theUniversal Microbial Diagnostics (UMD) platform, the inventors obtainedDNA oligonucleotides for the random DNA probes and their exactcomplements from Integrated DNA Technologies (Coralville, Iowa). Thesequences are provided below. MgCl₂, KCl, and sterile nuclease-freewater for making the molecular beacon (MB) buffer were purchased fromFisher Scientific (Waltham, Mass.). 1M Tris-HCl solution (pH 8.3) andTris-EDTA buffer (TE buffer; 10 mM Tris-HCl, 0.1 mM EDTA pH 8) wereobtained from Teknova (Hollister, Calif.). To prevent nucleasecontamination, all work surfaces and materials were routinely cleanedwith RNAse OFF decontamination solution (Takara, Japan).

Random probe 1: (SEQ ID NO: 1)5′-/5Cy5/CGA CGG TTG CTT GGG TAC TTG GAT GAT GCTAAA TTG GTG TTG GTC G/3Cy3Sp/-3′,  Random probe 2: (SEQ ID NO: 2)5′-/5Cy5/CGA CGG TGC TTT GAA TAC TTG GTA GAG GCTGGA GGG TGG TTG GTC G/3Cy3Sp/-3′,  Random probe 3: (SEQ ID NO: 3)5′-/5Cy5/CGA CGG TGC TGG GTG AAC TAA AGG GTG GGTGCT ATG GGA AGG GTC G/3Cy3Sp/-3′,  Random probe 4: (SEQ ID NO: 4)5′/5Cy5/CGA CTT AAT GAA TGT GTG GGC GCT TGG TTGCTT AAT GAG TGG GTC G/3Cy3Sp/-3′, and Random probe 5: (SEQ ID NO: 5)5′-/5Cy5/CGA CGT TTC TTT TCT GGA GGA GGG AGG GTTAGT TGT TAG GCA GTC G/3Cy3Sp/-3′. Random probe complement 1:(SEQ ID NO: 6) 5′-CGA CCA ACA CCA ATT TAG CAT CAT CCA AGTACC CAA GCA ACC GTC G-3′,  Random probe complement 2: (SEQ ID NO: 7)5′-CGA CCA ACC ACC CTC CAG CCT CTA CCA AGT ATT CAA AGC ACC GTC G-3′, Random probe complement 3: (SEQ ID NO: 8)5′-CGA CCC TTC CCA TAG CAC CCA CCC TTT AGT TCA CCC AGC ACC GTC G-3′, Random probe complement 4: (SEQ ID NO: 9)5′-CGA CCC ACT CAT TAA GCA ACC AAG CGC CCA CAC ATT CAT TAA GTC G-3′, andRandom probe complement 5: (SEQ ID NO: 10)5′-CGA CTG CCT AAC AAC TAA CCC TCC CTC CTC CAG AAA AGA AAC GTC G-3′.

Generation of random probe characteristic curves. The experimentallymeasured fluorescence resonance energy transfer (FRET), defined as theratio of Cy5 intensity over total fluorescence intensity (Cy3+Cy5), is afunction of the concentration of open random probes in the solution,i.e., the probe-target hybridization affinity. To discern thehybridization affinity between a probe and target in units of molarityrather than as a FRET ratio, a characteristic curve was constructed foreach probe. These curves presented the FRET ratio as a function of theconcentration of open probes in molarity.

To obtain the characteristic curves, random probes were diluted to 1 μMin 1× MB buffer (4 mM MgCl₂, 50 mM KCl, 10 mM Tris-HCl, pH=8, in sterileRNAse free water). DNA oligonucleotides perfectly complementary to therandom probes were diluted using 1× TE buffer to 10⁻⁵, 10⁻⁶, 8×10⁻⁷,6×10⁻⁷, 4×10⁻⁷, 2×10⁻⁷, 10⁻⁷, 8×10⁻⁸, 6×10⁻⁸, 4×10⁻⁸, 2×10⁻⁸, 10⁻⁸,10⁻⁹, 10⁻¹⁰, or 10⁻¹¹ M concentration. 25 μL of 1 μM random DNA probes(diluted in MB buffer) were added to 25 μL of perfect complement DNA ofvarious concentrations, or to the TE buffer-only control. The DNAmixture was briefly centrifuged with a mini centrifuge (VWR) to collectall DNA to the bottom of the tube. Then the DNA was hybridized using aMyCycler Thermal Cycler (Bio-Rad) under the following conditions: 95° C.for 5 minutes, 50° C. for 2 minutes, 30° C. for 1 minute, 20° C. for 1minute, and 4° C. for 2 minutes. 45 μL of each thermal cycled mixturewas added to 155 μL 1× MB buffer in a black flat bottom 96 well plate(Corning) and kept at 4° C. overnight. A non-linear optimizationalgorithm (22) was utilized to fit the parameters a, b, n and FRET₀ tothe characteristic curve: FRET(c)=FRET₀+a/(1+b (10⁻⁶−c)^(−n)) (FIG. 6).The coefficient of determination (R²) and root mean square error (RMSE)for the curve fits are reported in Table 1.

Bacterial DNA extraction. Overnight cultures of S. aureus USA 300 and E.coli MG1655 were used to inoculate fresh cultures grown in 50-100 mL 2×TY or Luria-Bertani (LB) broth, respectively. F. tularensis LVS wasobtained from Dynport Vaccine Company LLC (derived from NDBR101 Lot 4)and grown in modified Mueller-Hinton cation-adjusted (MHII) broth(Becton Dickinson) supplemented with sterile 0.1% glucose, sterile0.025% ferric pyrophosphate, and 2% reconstituted IsoVitaleX (BectonDickinston). Cultures were pelleted and washed three times with sterilePBS. To release chromosomal DNA cells were resuspended in TE buffer andmixed with 10% sodium dodecyl sulfate (SDS) and Proteinase K at 65° C.overnight. DNA was isolated using phenol: chloroform and precipitatedvia ethanol precipitation (protocol adapted from elsewhere (Sambrook etal., 2001)). DNA pellets were resuspended in 50 μL TE buffer and storedat −20° C.

Bacterial strains, C. jejuni, P. mirabilis, C. metallidurans, M. luteus,B. dentium, E. aerogenes, B. fragilis, and P. aeruginosa were grownovernight in 30 mL Brain Heart Infusion (BHI) media (BD) at 37° C.Bacterial cells were pelleted, washed two times with sterile 1× PBS, andresuspended in TE buffer. Proteinase K (1 mg/mL) (Sigma) and 0.5% SDSwere added to the bacterial cells, which were then incubated overnightat 55° C. on an orbital shaker. The samples were then mixed withphenol-chloroform (Invitrogen) and centrifuged; supernatants weretransferred to a fresh tube. This aqueous phase was then mixed with anequal volume of chloroform and centrifuged (and repeated). Finally, 1/10volume of 2 M sodium chloride and an equal volume of isopropanol wasadded to precipitate the DNA. This mixture was incubated at −20° C. for30 min and centrifuged. The pellets were rinsed with 70% ethanol, airdried, and resuspended in TE buffer.

Random probe and bacterial DNA hybridization. Bacterial DNA was dilutedto approximately 500 ng/μL using TE buffer and kept at −20° C. untiluse. The random MB probes were diluted to 1 μM in MB buffer prior touse. 25 μL of 1 μM random probes (diluted in MB buffer) was added to 25μL of TE buffer control and E. coli, F. tularensis, S. aureus, C.jejuni, P. mirabilis, C. metallidurans, M. luteus, B. dentium, E.aerogenes, B. fragilis, or P. aeruginosa DNA. The DNA mixture wasbriefly centrifuged to collect DNA and then hybridized using a MyCyclerThermal Cycler (Bio-Rad) under the following conditions: 95° C. for 5minutes, 50° C. for 2 minutes, 30° C. for 1 minute, 20° C. for 1 minute,and 4° C. for 2 minutes. 45 μL of each thermal cycled mixture was addedto 155 μL 1× MB buffer in a black flat bottom 96 well plate (Corning)and kept at 4° C. overnight.

Measuring FRET ratio through fluorescence as indicator of randomprobe-bacteria hybridization. The FRET ratio for the genomic DNA samplesfollowing hybridization with each of the random probes was determined byreading the Cy3 and Cy5 fluorescence using a Fluorolog-3spectrofluorometer (Jobin Yvon Horiba, Edison, N.J.) coupled with aMicroMax 384 MicroWell Plate Reader and water-cooled PMT detector.Samples were excited at 545 nm, and single point fluorescencemeasurements were taken with at 562 nm and 677 nm emission (optimalwavelengths determined through excitation-emission matrix analysis) tomeasure the Cy3 and Cy5 fluorescence, respectively. The FRET ratio wascalculated as Cy5/(Cy5+Cy3).

Determining DNA hybridization affinity via SantaLucia thermodynamicmodels. A comprehensive thermodynamic model by SantaLucia et al.(SantaLucia & Hicks, 2004) was utilized to predict the hybridization ofprobes to bacterial genomes. The SantaLucia model incorporatesthermodynamic parameters for mis-hybridizations between two DNAsequences. The inventors utilized two software packages: ThermoBlast DE,which performs fast alignment of sequences against large genomedatabases to discover thermodynamically stable hybridizations, andVisual OMP DE, which simulates hybridization experiments with detailedsolution conditions and generates results for melting temperature (Tm),Gibbs free energy (ΔG), and the percentage-based concentration of eachresultant species post-experiment. The secondary structure of eachmonomer, homodimer, and heterodimer species formed from the constituentprobes and target fragments can also be visualized.

To calculate the hybridization affinity of a genome to a probe, theinventors first used the ThermoBlast package and thermodynamicallyaligned the sequence of the random probe to both complement strands ofthe target genome. The inventors extracted all the sequence fragments ofthe genome (100-200 nucleotides) that aligned with the probe sequencewith a predicted melting temperature within approximately 35° C. of themelting temperature of the sequence genome. They then used Visual OMP DEto simulate the hybridization between the probe and the target genome,using the target fragments (FIG. 2). Every simulation containedinformation on the probe sequence, the target fragment sequences, andconditions for the experiment, including probe concentration (1 μM),unit target concentration (500 ng/μL for all bacteria), assaytemperature (4° C.), hybridization buffer composition (4 mM Mg++, 50 mMNa+, 0 M Glycerol, 0 M DMSO, 0 M Formamide, 0 M TMAC, 0 M Betaine), andpH 8. This procedure was repeated for each probe-target genome pair. Theinventors used the percentage of probe-target heterodimer structuresformed, i.e., the percentage of probes that are bound to targetfragments, as an estimate for the hybridization affinity of the probe toeach target (FIG. 2B).

Linearity assumption considerations in UMD. In the UMD platform, theprobe concentration (1×10⁻⁶ M) is in far excess of the targetconcentrations (˜1×10⁻¹⁰ M); therefore, the inventors are able tolinearly combine the hybridization affinity signatures that they measurefor individual targets using the hybridization model. Due to theflooding of excessive number of probes, each target fragment has itschoice of binding/not binding to the probes, and thus the inventors cansafely sum together multiple target interactions of the same probe,assuming them to be independent.

Receiver-operator curve (ROC) analysis. Receiver-operator curve (ROC)analysis was performed by plotting a ROC curve showing the sensitivityand (1-specificity) for 1000 threshold values ranging from −1 to 1. Foreach threshold value, the following procedure was performed on the datamatrix of normalized inner products between the experimentally obtainedhybridization affinity and predicted hybridization affinities (bythermodynamic model) for the nine independent bacterial DNA samples(FIG. 3C): Each entry in the inner product data matrix was compared withthe threshold value to determine the number of true positives, falsepositives, true negatives, and false negatives. True positives wereidentified when values in the diagonal entries of the inner product datamatrix were greater than the threshold value since diagonal entriesrepresent the correct classification of the bacterial sample with itscorresponding genus in the database. False positives were identified asoff-diagonal values that were greater than the threshold value. Truenegatives were identified as off diagonal values that were less than orequal to the threshold value. False negatives were identified asdiagonal values that were less than or equal to the threshold value. Foreach threshold value, sensitivity was defined as (# true positives/(#true positives+# false negatives)) and specificity was defined as (#true negatives/(# true negatives+# false positives)).

Greedy probe selection. Given a set of P random probes, finding the setof M probes with the best detection performance in terms of sensitivityand specificity is an extremely challenging problem. A brute forcesearch would require one to search among all

$\quad\begin{pmatrix}M \\P\end{pmatrix}$

possible combination of M probes to find the optimal probe set. Thiscombinatorial search algorithm grows quadratic with P and thus becomescomputationally intractable when the number of probes grows. Theinventors thus developed a rapid probe selection method that theinventors dub Greedy Probe Selection (GPS). With a small sacrifice insensitivity, GPS finds the best performance probe in a few seconds:exponentially faster than the naïve search method. The algorithm in eachiteration finds the probe that maximizes a detection performancecriterion (here the maximum pairwise correlation of bacteria) and addsit to the list of probes picked from the previous iterations. GPS stopswhen the maximum desired number of probes is reached.

Mathematical formulation of the compressive sensing (CS) detection andestimation algorithms. First, the inventors set up a mathematical model.Recall that there are N target bacteria of interest. Each microbialsample is characterized by a concentration vector x=[x₁, x₂, x₃, . . . ,x_(N)]^(T) containing the concentration x_(i) of bacterium i. While thetotal number of target species N might be large, a typical contaminatedor infected sample will contain only a few target species K withsignificant concentration. When N»K, the inventors say that the vector xis sparse. Experimental quantification of the amount of hybridizationbetween the DNA of the microbial sample and M random probes produces theprobe-binding vector y=[y₁, y₂, y₃, . . . , y_(M)]^(T) containing thehybridization binding level y_(j) of the sample to probe j. Bypredicting the hybridization binding level φ_(ij) of probe i tobacterium j from the database of targets of interest, one can form theM×N hybridization affinity matrix Φ (FIG. 1B and FIG. 2B). The inventorsrefer to column i of the matrix Φ by the column vector φ_(ij).

As described in above, the probe-binding vector y will approximatelyform as a linear combination of the predicted hybridization affinitiesof the species in the reference genome database (the columns of thematrix Φ) weighted by their concentrations x, i.e.:

y=Φx+n

Here the vector n accounts for noise and modeling errors.

Two goals of UMD are to detect the presence and estimate theconcentrations x of a potentially large number N of reference microbialgenomes in a sample given only a small number M×N of probe-bindingvector measurements y. Simply inverting the matrix Φ is impossible inthis case, since it has many more columns than rows. Fortunately, it isreasonable to assume that only a small number K of microbial genomeswill be present in a given sample, in which case the concentrationvector x is sparse with K nonzero and N−K zero (or close to zero)entries; when K<M, one can hope to invert Φ to estimate the K nonzeroconcentrations. In order to apply the standard compressive sensingtheory, the columns of Φ should satisfy the so-called RestrictedIsometry Property (RIP). It has been shown that a matrix satisfies theRIP if its columns are sufficiently incoherent (Baraniuk, 2011), i.e.,when the largest normalized inner product between any two columns of Φ:

${\mu = {\min\limits_{{\forall i},{j:{i \neq j}}}\frac{\phi_{i}^{T}\phi_{j}}{{\phi_{i}}_{2}{\phi_{j}}_{2}}}},$

known as the coherence, is bounded above by a small constant. Morespecifically, it has been shown (Donoho & Xiaoming, 2001) thatμ<1/(2K−1) is a sufficient condition to exactly recover a K-sparsesignal with only M=cK log(N/K), where c is a small constant,measurements in the noise-free scenario when n=0.

In the presence of noise, the same sufficiency bound holds if themagnitudes of the non-zero elements of x are sufficiently large comparedto the noise variance (Cal & Wang, 2011). When these conditions hold,the inventors can both detect the presence of bacteria and estimatetheir concentration using standard CS signal recovery algorithms. In theUMD platform, both the hybridization matrix Φ and the sparseconcentration vector x are non-negative. A more optimistic recoverybound has been proven to hold in this regime. In particular, analternative notion of incoherence is defined (Bruckstein et al., 2008)that improves the recovery guarantee. For an arbitrary matrix Φ, theone-sided coherence is defined as:

${\rho (\Phi)} = {\min\limits_{{\forall i},{j:{i \neq j}}}{\frac{\phi_{i}^{T}\phi_{j}}{{\phi_{i}}_{2}^{2}}.}}$

It is shown (Bruckstein et al., 2008) that, for a non-negative matrix Φ,if a nonnegative K-sparse solution exists such that ρ(PD)<1/(2K−1), thenthe solution is unique and CS recovery algorithms can find it. Here D isdefined as the column L₁ (sum)-normalized matrix of Φ, and P is thecolumn mean subtraction operator (Bruckstein et al., 2008). While theone-sided coherence recovery bound is based on the better-conditionedmatrix PD, it remains pessimistic, and better recovery performance istypically achieved in practice. The concentration vector x is recoveredfrom the measurement vector y via a sparsity-penalized optimization ofthe form

${\min\limits_{x}{x}_{0}},{{{subject}\mspace{14mu} {to}\mspace{14mu} {{y - {\Phi \; x}}}_{2}} < {\sigma.}}$

Here ∥x∥₀, known as the L₀-norm, counts the number of non-zero values inthe vector x, and Φ estimates the noise standard deviation. While thisoptimization problem has exponential complexity, a variety of differentgreedy algorithms have been developed to solve it approximately.Orthogonal Matching Pursuit (OMP) (Tropp & Gilbert, 2007) is aniterative greedy algorithm that, at each step, selects the column of Φthat is most correlated with a residual vector from the previousiteration. The primary advantages of OMP are its simplicity and fastconvergence. Moreover, if the sparsity level K (the number of bacteria)is known, then the algorithm can use it as the stopping criterion. Theinventors can leverage the fact that both the hybridization affinitymatrix Φ and the sparse concentration vector x are non-negative toimprove the performance of OMP. The inventors utilize the variant of OMP(Bruckstein et al., 2008) that is adapted to recover non-negative sparsesolutions from non-negative sensing matrix Φ. Instead of directlyworking on Φ, this algorithm operates on the canonical matrix PD, whereD is the column L₁-norm normalized version of matrix Φ defined as:D=ΦW⁻¹, where W is an M×M diagonal matrix containing the column sums ofthe hybridization matrix Φ and P is the pre-conditioner matrix and canbe chosen as any invertible N×N matrix. In the case of a positive matrixD, an efficient preconditioning can be obtained by subtracting theweighted mean of each column of D: PD=(1−(1−ε)E/N)D, where E is N×Nmatrix of ones, I is the identity matrix, and 0<ε«1 is a weighingconstant to make the P matrix invertible. Working with thepreconditioned matrix PD does not change the solution of the problem(Bruckstein et al., 2008); however, it significantly improves the OMPalgorithm behavior and performance guarantees. This OMP algorithmvariant is given in Alg. 1 (Bruckstein et al., 2008):

Algorithm 1: Non-negative OMP Data: D, y Result: x^(t) Initialization;The temporary solution: x_(i) = 0; The temporary residual: r^(i) = y;The temporary solution support: S^(i) = support{x^(i)} = φ. While   ||r||₂ ² ≤ T       do       Sweep:          ∀j ∈ [1,2, 

 , N]:find ε(j) = min_(x≥0)||D_(j)x_(j) − r||₂ ²       Update support:find j₀ such that ∀j ∈S^(i), ε(j₀) ≤ ε(j);       update S^(i) = S^(i−1)∪ {j₀};       Update solution: compute          x^(i) : x^(i) =min_(x≥0)||Dx − y||₂ ² subject to          support{x^(i)} = S^(i);      Update residual: compute r^(i) = y − Dx^(i) ; End

The inventors set the stopping criterion to T=2×10⁻¹ for recovering theexperimentally obtained hybridization affinity vectors in FIG. 3 and thenumerically simulated hybridization affinity vector corrupted bymodeling noise level in FIG. 4. T=7×10⁻³ was selected to recover thesimulated hybridization affinity vector corrupted by the experimentalnoise level simulations in FIG. 4. In reporting the similarity of theexperimentally measured hybridization affinity vectors in FIG. 3D, theinventors have reported the similarity of measured hybridizationaffinity vector y in each experiment to the bacteria i in the dictionaryas the inner product of the normalized affinity vector y/∥y∥₂ and thei_(th) column of the normalized preconditioned matrix PD. The innerproduct is a unit-less number in the range [−1,1], where 1 indicates thehighest similarity and −1 the lowest similarity.

Complete list of bacterial strains used in UMD simulations. To evaluatethe UMD platform for genus level bacterial detection, the inventorsselected 40 species from 40 different genera that are listed among mostcommonly pathogenic to humans by the Center for Disease Control andPrevention (CDC). The genome sequences of the following strains wereobtained from the NCBI website:

Acinetobacter baumannii ATCC 17978,

Aeromonas salmonicida subsp. salmonicida A449,

Bacteroides fragilis 638R,

Bacillus cereus ATCC 14579,

Bartonella henselae str. Houston-1,

Bifidobacterium dentium Bd1,

Bordetella pertussis Tohama,

Borrelia burgdorferi B31,

Brucella abortus S19,

Campylobacter jejuni subsp. jejuni 81116,

Clostridium botulinum B1 str. Okra,

Corynebacterium jeikeium K411,

Coxiella burnetii RSA 331,

Cupriavidus metallidurans CH34,

Enterobacter aerogenes EA1509E,

Enterococcus faecalis V583,

Escherichia coli str. K-12 substr. MG1655,

Francisella tularensis subsp. holarctica LVS,

Fusobacterium nucleatum subsp. nucleatum ATCC 25586,

Haemophilus influenzae F3047,

Helicobacter pylori B38,

Klebsiella pneumoniae 342,

Legionella pneumophila str. Corby,

Leptospira interrogans serovar Copenhageni str. Fiocruz L1-130,

Listeria monocytogenes 08-5578,

Micrococcus luteus NCTC 2665,

Mycobacterium leprae TN,

Mycoplasma pneumoniae M129,

Neisseria meningitidis MC58,

Prevotella melaninogenica ATCC 25845,

Propionibacterium acnes KPA171202,

Proteus mirabilis HI4320,

Pseudomonas aeruginosa LESB58,

Rickettsia rickettsii str. Iowa,

Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150,

Serratia proteamaculans 568,

Shigella sonnei Ss046,

Staphylococcus aureus subsp. aureus USA300 FPR3757,

Vibrio cholerae MJ-1236, and

Yersinia pestis CO92.

For species-level bacterial detection, the following 24 differentstrains from the Staphylococcus genus were selected to perform thesimulations:

Staphylococcus arlettae CVD059 SARL-c1,

Staphylococcus aureus subsp. aureus NCTC 8325,

Staphylococcus auricularis strain DSM 20609,

Staphylococcus capitis subsp. capitis strain AYP1020,

Staphylococcus carnosus subsp. carnosus TM300,

Staphylococcus cohnii subsp. cohnii strain 532,

Staphylococcus epidermidis ATCC 12228,

Staphylococcus equorum strain KS1039,

Staphylococcus gallinarum strain DSM 20610,

Staphylococcus haemolyticus JCSC1435,

Staphylococcus hominis subsp. hominis C80,

Staphylococcus hyicus strain ATCC 11249,

Staphylococcus lentus F1142 s6-trimmed-contig-1,

Staphylococcus lugdunensis HKU09-01,

Staphylococcus massiliensis CCUG 55927,

Staphylococcus pettenkoferi VCU012,

Staphylococcus pseudintermedius HKU10-03,

Staphylococcus saprophyticus subsp. saprophyticus ATCC 15305,

Staphylococcus schleiferi strain 2317-03,

Staphylococcus sciuri subsp. sciuri strain DSM 20345,

Staphylococcus simiae CCM 7213 contig00565,

Staphylococcus simulans ACS-120-V-Sch1,

Staphylococcus vitulinus F1028 S-vitulinus-F1028-0001, and

Staphylococcus warneri SG1.

For species-level bacterial detection, the following 23 differentstrains from the Vibrio genus were selected to perform the simulations:

Vibrio anguillarum 775,

Vibrio brasiliensis LMG 20546 VIBR0546-99,

Vibrio cholerae O1 biovar El Tor str. N16961,

Vibrio cyclitrophicus FF75 Ctg1,

Vibrio diazotrophicus NBRC 103148,

Vibrio ezurae NBRC 102218,

Vibrio furnissii NCTC 11218,

Vibrio genomosp. F6 str. FF-238,

Vibrio genomosp. F10 str. ZF-129,

Vibrio harveyi ATCC BAA-1116,

Vibrio litoralis DSM 17657,

Vibrio maritimus strain: JCM 19235,

Vibrio metschnikovii CIP 69.14 VIB.Contig153,

Vibrio nigripulchritudo str. SFn1,

Vibrio orientalis CIP 102891 ATCC 33934 strain CIP 102891,

Vibrio pacinii DSM 19139 BS19DRAFT-scaffold00001.1-C,

Vibrio parahaemolyticus RIMD 2210633,

Vibrio proteolyticus NBRC 13287,

Vibrio rhizosphaerae DSM 18581,

Vibrio scophthalmi LMG 19158 VIS19158-99,

Vibrio splendidus LGP32,

Vibrio tubiashii ATCC 19109, and

Vibrio vulnificus YJ016.

Example 2 Results for UMD Using MBs

Experimental proof of concept. To prove the UMD concept, the inventorsmixed five UMD MBs (as shown in FIG. 2A and characterized in FIG. 6 withGC-contents 50, 56.5, 60.8, 50, and 52.7%, identical melting temperatureof 40° C., and concentration 1 μM) in separate tubes (to prevent crosshybridization of probes) with genomic DNA from each of nine humaninfectious bacterial strains grouped into three categories: I. Exactsequence known (Escherichia coli, Francisella tularensis, Staphylococcusaureus, Campylobacter jejuni, and Proteus mirabilis), II. Exact sequenceunknown (Cupriavidus metallidurans and Micrococcus luteus), and III.Clinical isolates, whose exact sequence is unknown (Bacteroides fragilisand Enterobacter aerogenes) The identification of Pseudomonas aeruginosaand Bifidobacterium dentium strains, was tested using four random probes(see FIGS. 7-9 for detection results). For bacteria in groups II andIII, the DNA sequences in the database might not exactly match thesequences present in the bacterial samples.

For each MB-bacterial species pair, equal volumes of probe and bacterialDNA were combined and subjected to a thermal cycling process ofdenaturing (95° C.) and binding/cooling to 4° C. overnight. To quantifyprobe-DNA binding, the MB probes' Cy3 and Cy5 fluorescence intensitieswere measured with a fluorometer, and the fluorescence resonance energytransfer (FRET) ratios (a decrease represents MB opening due to DNAbinding) were calculated by computing Cy5 intensity over totalfluorescence intensity (Cy3+Cy5). The FRET ratios from binding of thenine bacteria to the MBs are depicted in FIG. 3A.

In order to estimate the bacterial concentrations in physical units, theinventors translated the FRET ratio of each bacterium-MB pair into theconcentration of opened MBs or hybridization affinity, represented inunits of molarity. For this, they experimentally obtained and fittedFRET ratios for each of the five MBs as a function of the concentrationof their exact probe complements, using an optimization method describedin (Jeričević and Kušter, 2005) (FIG. 6) (see Table 1 and Example 1 forthe fit curve parameters and fit method, respectively). The R² valuesfor the fits ranged from 0.97 to 0.99, suggesting a satisfactory fit.Based on the fit equations, the hybridization affinities correspondingto the FRET ratios for all bacteria were calculated. The inventors referto these measurements as the measured hybridization affinity vectors andshow them in FIG. 3B. The challenge was to decode the experimentallymeasured affinities of the bacterial species samples reacting to UMD MBsusing compressive sensing recovery techniques. With the predictedhybridization affinities of N=9 bacteria to M=5 random probes stored inthe computationally obtained Φ_(5×9), the inventors used a variant ofthe Orthogonal Matching Pursuit (OMP) algorithm (Bruckstein et al.,2008) and successfully identified the species present in each of thesamples (FIG. 3). UMD estimated the relative bacterial concentrationswith an average error of 11.5% (FIG. 10).

To provide the physician or scientist with a metric quantifying howclose the measured hybridization affinity vector is to that of eachbacteria in the database and thus how confident the OMP detectionresults are, UMD can output the inner products between the normalizedmeasured hybridization affinity vectors from the nine experiments andthe columns of the centered and normalized matrix Φ_(5×9) (FIG. 3C).This metric measures the similarity of a pathogenic sample to bacteriain the UMD database. Using this metric, the inventors characterized theperformance of UMD in identifying the nine pathogens in terms of falsepositives and false negatives. The inventors constructed the receiveroperating characteristic (ROC) curve (FIG. 3D), where each point on thecurve corresponds to a certain universal detection threshold in therange [−1,1] for all nine independent bacterial experiments. Innerproduct values above/below the detection threshold were considered as apositive/negative outcome, respectively. The area under thecorresponding ROC curve (AUC=0.91) suggests successful screeningperformance. FIG. 3E additionally shows the consistency of the measuredand simulated hybridization affinities of nine bacteria to five randomprobes (different probes are shown in different colors). The normalizedroot-mean-square error NRMSE=12%, suggests that the inventors'thermodynamic modeling of bacteria-probe hybridization is accurate.

Next, the inventors assessed the performance of UMD in detecting thesenine test species from a list of common pathogens using the five DNAprobes. The inventors expanded the reference genome database to contain40 genera (i.e., M=5«N=40), including bacterial pathogens listed by theCenters for Disease Control and Prevention (CDC) as the most commonnotifiable human diseases (Centers for Disease Control and PreventionMorbidity and Mortality Weekly Report, 2013). With the most commonpathogens' genomes in the database, the detection performance remainedabove AUC=0.84, suggesting a high recovery rate with only five randomprobes.

Extension by simulation. Thus far, the inventors have presented anexperimental proof-of-concept that validates UMD's ability to detecteleven test species among a list of pathogens using a fixed set of fiverandomly selected test probes. The inventors next numericallydemonstrate that, if a sufficient number of probes is used, then anygroup of randomly selected probes will detect the presence of one (K=1)or a mixture of several (K=2, 3, . . . ) pathogenic organisms in asample out of a database of 40 pathogenic organisms. They introducedadditive white Gaussian noise to the simulated hybridization affinityvectors to capture the variance in the hybridization affinities amongthe independent test trials in FIG. 3. The noise levels wereextrapolated from the above eleven test bacteria experiments, with thenoise variance set to σ₀=2.4×10⁻⁸ M. In order to control for differencesin the genome size of each organism, the inventors normalized numericalsimulations to unit weight of bacterial DNA.

In FIG. 4A, the inventors first demonstrate the detection performance ofUMD in identifying a single bacterium (K=1) among the pathogen databaseat different noise levels. As the ROC curves suggest, UMD's detectionperformance improves when the noise variance decreases. With only afive-fold decrease in the noise variance, UMD identifies all 40 bacteriain the database almost perfectly (AUC>0.95) using only five randomlyselected probes.

The ability of UMD to universally detect target species can be improvedby increasing the number of random probes. FIG. 4B demonstrates that UMDidentifies all 40 bacteria in the CDC database almost perfectly(AUC=0.95) with any M=15 randomly selected MBs when the noise varianceis similar to that measured experimentally (FIG. 3).

UMD has the unique advantage that it can recover more than a single(K>1) organism in an infectious sample. To evaluate the minimum numberof probes M required for this task, the inventors used the Basis PursuitDe-Noising (BPDN) algorithm (as described in the Supplementary

Materials) to identify the composition of a sample containing K={2,3}equi-concentration bacterial species (FIG. 4C). The inventors found thatany set of M=15 randomly selected probes will recover all

possible mixtures of K={2,3} pathogenic species in the CDC database. Theerror bars show the standard deviation over 1000 test trials withdifferent sets of random MBs. This result confirms that the incoherencerequirement for compressive sensing is empirically satisfied for thepathogenic strains in the CDC database and thus that UMD is capable ofscreening for pathogenic bacteria at the genus-level.

The inventors next evaluated UMD's performance for species-levelbacterial detection. They focused on differentiating among 24 species ofStaphylococcus genus and 23 species of Vibrio genus in silico. Theyidentified the composition of samples containing Staphylococcus speciesusing 11 random probes (FIG. 11A) and the composition of samplescontaining Vibrio species using 18 random probes (FIG. 11B) with highsensitivity and specificity (AUC>0.95). This underscores UMD's potentialto differentiate pathogens at high taxonomic resolution.

Using the UMD platform, one can trade off between universality(detecting species outside of the library) and cost efficiency (numberof probes). That is, it is possible to select a set of probes thatachieves better detection performance in terms of specificity andsensitivity than the average performance of random probe sets at thecost of universality. For example, in FIG. 4C, some of the probe setsachieved 100% accuracy using three fewer probes than the number requiredfor universal recovery. To capitalize on this phenomenon, the inventorsdeveloped a “greedy probe selection” (GPS) algorithm that rapidlyselects these optimized probes given a very large database ofgenome-probe hybridization affinities.

FIGS. 4D-E, illustrate the UMD confusion matrices in detectingpathogenic bacteria using M=3 and M=10 probes selected using GPS. Thefalse positive rate drops for all of the bacteria in the database as thenumber of probes increases from M=3 (AUC>0.95) to M=10 (AUC>0.99). Whilethe performance detection is high (AUC>0.99), the confusion matrix showsfew cases where the inner product values for possible species (e.g.,Coxiella, Aeromonas, and Proteus when the actual sample containsCoxiella) are only slightly separated. FIG. 12 shows that greaterseparation between inner product values for candidate bacterial speciescan be achieved by using a larger number of UMD probes. This canincrease the robustness of the UMD system (ensure low false positiverate) for noisier environments.

While mainly intended to rapidly screen for pathogens at higher taxonomylevels, UMD can also provide strain-level information to the physicianusing additional GPS-selected probes. In FIG. 13, the inventorsdemonstrate that GPS selects UMD probes that differentiate among 9strains of E. coli (8 pathogenic and one nonpathogenic) with highdetection accuracy (AUC>0.95) in silico.

The theory behind the UMD can be extended to identify more complexsamples using a relatively small number of random probes. The inventorsverified that the UMD platform can recover complex microbial samplescontaining up to one hundred active species out of a large dictionary ofN=1500 bacterial genera. They first computed the hybridization affinityof a set of random probes to N=1500 representative species of allsequenced bacterial genera in NCBI website. Then the inventors used theresulting hybridization affinity matrix to identify the composition ofsamples containing K unique species with equal concentrations (the mostdifficult case where the sample contains multiple species all atsignificant concentration). For each value of K, FIG. 14 shows theminimum number of random probes M required to identify the compositionof 1000 simulated complex samples containing K species randomly selectedfrom N=1500 genera. FIG. 14 illustrates that the number of requiredprobes M closely (R²=0.98) follows the compressive sensing theory M=cKlog(N/K) with constant c=2.94. That is, UMD requires a number of probesthat grows logarithmically with the number of target bacteria N andsub-linearly with the number of active bacteria K in the sample.

Example 3 Discussion of UMD Using MBs

UMD probes are universal in the sense that a fixed set of probescaptures the salient information required to distinguish between membersof a large and growing database of species (Davenport et al., 2010).This gives UMD a potentially important future proof property: a fixedset of measurement probes can be used to detect and estimate theconcentration of newly sequenced species not yet present in the library.To detect a new organism, the software merely has to be adjusted to takeinto account how the new organism will react to the existing probe set;however, new capture probes are not required. Moreover, since the numberof probes grows only logarithmically in the size of the library, the UMDplatform naturally contends with the data deluge (Baraniuk, 2011) fromnew microbial species being discovered and sequenced every day.

Several other pathogen detection schemes are currently underinvestigation (Chakravorty et al., 2010, Dai et al., 2009; Mohtashemi etal., 2011 and Chung et al., 2013). To the best of the inventors'knowledge, UMD is the only technique that enables a unifiedrepresentation of bacterial organisms in a low-dimensional geometricspace. The theory of compressive sensing provides rigorous recoveryguarantees and suggests algorithms to leverage this geometry to bothdetect bacteria and estimate their concentrations efficiently. Theinventors' successful implementation of UMD confirms that a small numberof random DNA probes satisfy the incoherency requirements of compressivesensing theory and can be used for universal microbial sensing.

The UMD platform has the potential to rapidly direct physicians to useappropriate antibiotics or treatment and thus minimize the risk ofantibiotic resistance. It can also be utilized in biodefenseapplications to classify multiple novel and mutant agents. With furtheroptimization of the probe design and detection schemes, the inventorsexpect that UMD will be able to sense an even wider range of organisms(e.g., viruses, fungi) and various biomolecules of interest (genes,proteins).

Finally, the theory behind the UMD platform can be applied to DNAsensing in several other incarnations, including reads from a sequencer,e.g., one may be able to quickly identify a bacterium from a subset ofreads, rather than requiring full alignment or assembly. Application ofsuch signal acquisition principles to biological sensing systems willshape the future of microbial diagnostics.

Example 4 Use of the Insense Algorithm for Probe Selection

The Insense algorithm (Alg. 1; FIG. 16) was experimentally validatedusing a range of synthetic and real-world datasets. In all experiments,the following were set: ∈₁=10⁻⁹ and ∈₂=10⁻¹⁰ (anything in the range∈₂<∈₁«1 can be utilized). Insense was terminated when the relativechange of the cost function μ_(avg) ²(Φ_(Ω)) dropped below 10⁻⁷.

Baselines and performance metrics. Insense was compared with severalleading sensor selection algorithms, including Convex Sensor Selection(Joshi & Boyd, 2009), Greedy Sensor Selection (Shamaiah et al., 2010),EigenMaps (Ranieri et al., 2012), and FrameSense (Ranieri et al., 2014).It was also compared with four greedy sensor selection algorithms thatwere featured in (Ranieri et al., 2014). The first three minimizedifferent information theoretic measures of the selected sensing matrixas a proxy to the MSE: the determinant in Determinant-G (Steinberg &Hunger, 1984), mutual information (MI) in MI-G (Krause et al., 2008),and entropy in Entropy-G (Wang et al., 2004). The final greedyalgorithm, MSE-G (Das & Kempe, 2008; Golovin et al., 2010; Das & Kempe,2011), directly minimizes the MSE of the LS reconstruction error. Thecodes for these baseline greedy algorithms were obtained on the worldwide web at github.com/jranieri/OptimalSensorPlacement. A comparison wasalso made with Random, a simple baseline that selects sensors at random.

The sensor selection algorithms were compared using the following sixmetrics:

Average coherence μ_(avg) (Φ_(Ω)).

Maximum coherence μ_(max) (Φ_(Ω)).

Frame potential FP(Φ_(Ω)) (see (3))

Condition number CN(Φ_(Ω)).

BP recovery accuracy.

Running time.

Depending on the task, in some experiments only a subset of the metricsare reported. To compute BP recovery accuracy, the performance of the BPalgorithm (Chen et al., 1998) was averaged over multiple trials. In eachtrial, a K-sparse vector x whose non-zero entries are equal to one wasfirst generated. Then, BP was used to recover x from linear, nonadaptive(noiseless) measurements y=Φ_(Ω)x.

The same experiment was repeated for all

$\quad\begin{pmatrix}N \\K\end{pmatrix}$

sparse vectors x witn mtterent support sets and the percentage of trialsthat x has been exactly recovered was reported. When

$\quad\begin{pmatrix}N \\K\end{pmatrix}$

is too large (here, greater than 10,000), the BP algorithm was run on asmaller random subset or all

$\quad\begin{pmatrix}N \\K\end{pmatrix}$

sparse vectors x.

Unstructured synthetic datasets. The sensor section algorithms werefirst tested by applying them to random matrices. It is easy to showthat asymptotically (i.e., when N→∞) random matrices do not favorcertain rows (sensors) over others. In the non-asymptotic regime (i.e.,when N is finite) the choice of sensors for sparse recovery might becritical, since the probability that certain sets of sensorssignificantly outperform others increases. In this case, the sensorselection algorithm aims to identify these high-performing sensors.Three types of random sensing matrices Φ whose entries are drawn fromGaussian, Uniform, and Bernoulli distributions were generated and theperformance of Insense was compared against the other baselines.

1) Random Gaussian matrix: This experiment was conducted for 20 randomtrials and 100×100 matrices Φ whose entries are independently drawn froma standard normal distribution were generated. Insense and otherbaseline algorithms were used to select M ∈ {5, 6, 7, . . . , 30}sensors. In FIGS. 17A&B, μ_(avg) and μ_(max) of the selectedsub-matrices Φ_(Ω) are reported, with |Ω|=M. All of the sensor selectionalgorithms have comparable performance to the random sensor selectionstrategy (Random), illustrating that only small improvements to themaximum and average coherence can be achieved using these algorithms.

2) Random Uniform matrix: The previous experiment was repeated with asensing matrix Φ whose entries were drawn uniformly at random from [0,1]. FIG. 17C shows that Insense outperforms most of the baselinealgorithms, including Random and Convex SS, in terms of μ_(avg). Despiteselecting completely different sensors, FrameSense and EigenMaps havecomparable performance to Insense in minimizing μ_(avg). FIG. 17D makesapparent the gap in maximum coherence μ_(max) between that achieved byInsense and the other baselines.

3) Random Bernoulli matrix: The previous experiment was repeated with asensing matrix Φ whose entries are 0 or 1 with equal probability. Inthese experiments, the coherence minimization performance of all of thesensor selection algorithms was similar for Bernoulli (1/−1) matricesand Gaussian matrices. FIG. 17E shows that FrameSense, Eigen-Maps, andInsense have similar performance and outperform the other algorithms bya large margin in terms of average coherence μ_(avg). When a selectedmatrices (ton contains one column with all zero entries, the averagecoherence μ_(avg) is not defined. The missing values in some curvescorrespond to these instances. FIG. 17F shows a clear gap betweenInsense and the other baselines in terms of the maximum coherenceμ_(max).

In summary, Insense selects reliable sensors that are consistentlybetter than or comparable to the other baseline algorithms on randomsensing matrices. This suggests that Insense could find application indesigning sensing matrices that outperform random matrices for CSrecovery tasks.

Highly structured synthetic datasets. In contrast to random matrices,the sensing matrices in real-world applications often have imposedstructures or redundancies. In such cases, careful sensor selection canmean the difference between low and high performance. Sensor selectionwas explored with structured over-complete matrices by constructing twosynthetic datasets that resembled the redundancies and structures inreal-world datasets. Similar over-complete basis has been explored inChen et al. (1998).

1) Identity/Gaussian matrix: The first highly structured dataset wasconstructed by concatenating two 50×50 matrices: An identity matrix anda random matrix with i.i.d. Gaussian entries. Such matrices featureprominently in certain real-world CS problems.

For instance, in the universal DNA-based microbial diagnostics platformstudied in Aghazadeh et al. (2016), the identity and Gaussian matricessymbolize two different types of sensors: the identity matrixcorresponds to a set of sensors that are designed to be specific to asingle microbial target (column) in the dictionary Φ, while the Gaussianmatrix corresponds to a set of sensors that are universal for microbialtargets in the dictionary. As in Aghazadeh et al. (2016), consider abacterial detection scenario where the solution to the sparse recoveryproblem both detects and identifies the bacterial targets in a sample(through the support of the sparse vector x); here the goal is tomaximize the average sparse recovery (detection) performance. On the onehand, if all of the sensors are selected from the identity submatrix,then nearly all of the selected sensors will lie dormant when detectinga particular bacterial target. On the other hand, if the sensors areselected from the Gaussian submatrix, then the selected sensors willwork jointly to detect all bacterial targets, which provides bothuniversality and better average sparse recovery performance (Aghazadehet al., 2016). To achieve a better sparse recovery performance, thesensor selection algorithm should select rows (sensors) from theGaussian submatrix rather than the identity submatrix.

Table 2 compares the performance of Insense to the baseline algorithmsfor the problem of selecting M=10 rows from the structuredIdentity/Gaussian Φ. The same experiment was repeated10 times withdifferent random Gaussian matrices. (Dashes correspond to instanceswhere the selected matrices Φ_(Ω) contain columns with all zero entries;here the average coherence μ_(avg) is undefined.) In particular,Insense, Convex SS, and MSE-G are the only algorithms that select rowsof the Gaussian sub-matrix. While achieving the minimum FP(Φ_(Ω)) (=0),the other algorithms perform poorly on BP recovery. The greedyalgorithms select rows from the identity matrix that result in columnswith allzero entries and thus fail to recover most of the entries in x.Digging deeper, Insense selects rows with smaller column coherence thanConvex SS and MSE-G. As a result, Insense achieves the best BP recoveryperformance (Table 2) among these three algorithms.

In summary, this example demonstrates that minimizing a similaritymetric imposed on the rows of the sensing matrix (such as framepotential, etc.) will not maximize the recovery performance of sparsesignals. These results also provide reassurance that the coherence amongthe columns of the sensing matrix is a useful performance objective.

TABLE 2 Comparison of Insense against the baseline algorithms onselecting M = 10 rows from a structured identity/Gaussian Φ. Insenseselected the set of sensors with the smallest μ_(avg) and achieved thebest BP recovery performance. μ_(avg)(Φ_(Ω)) FP(Φ_(Ω)) CN(Φ_(Ω)) BPaccuracy % Insease 0.3061 ± 0.0047 1019 ± 313  1.93 ± 0.19 92.27 ± 1.42 FrameSense — 0.00 ± 0.00 1.00 ± 0.00 4.00 ± 0.00 EigenMaps — 0.00 ± 0.001.00 ± 0.00 4.00 ± 0.00 MSE-G 0.3872 ± 0.0305 1155 ± 374  11.51 ± 0.93 57.91 ± 1.09  MI-G — 0.00 ± 0.00 1.00 ± 0.00 4.00 ± 0.00 Entropy-G —0.00 ± 0.00 1.00 ± 0.00 4.00 ± 0.00 Determinant-G — 0.00 ± 0.00 1.00 ±0.00 4.00 ± 0.00 Greedy SS — 0.00 ± 0.00 1.00 ± 0.00 4.00 ± 0.00 ConvexSS 0.3137 ± 0.0075 2279 ± 470  2.22 ± 0.25 88.64 ± 3.64 

2) Uniform/Gaussian matrix: To study the quality of the box constraintrelaxation in (6), Insense was compared against the baseline algorithmsfor a matrix Φ where the globally optimal index set of rows (sensors) Ωis known. (For arbitrary Φ, global combinatorial optimization iscomputationally intractable when D, N>200 or so.)

A 10×200 matrix was concatenated with i.i.d. Gaussian entries and a190×200 matrix with i.i.d. [0, 1] uniform distribution entries. In thiscase, one would expect that the Gaussian submatrix has the lowestμ_(avg) when M=10. FIG. 18 visualizes the results of running Insense andthe Convex SS baseline algorithm on such a Φ. In all 10 random trials,Insense successfully selected all Gaussian rows and hence found theglobally optimal set of sensors. FrameSense and EigenMaps miss, onaverage, 10-20% of the Gaussian sensors. The other baselines algorithms,including Convex SS, select only a small portion (<20%) of the Gaussianrows (sensors). Table 3 also indicates that Insense achieves better BPrecovery performance, since it selects exclusively Gaussian rows,resulting in the minimum average coherence μ_(avg) of the resultingsensing matrix.

TABLE 3 Comparison of Insense against the baseline algorithms onselecting M = 10 rows from a structured uniform/Gaussian Φ. Insenseselected the set of sensors with the smallest μ_(avg) and achieved thebest BP recovery performance. μ_(avg)(Φ_(Ω)) FP(Φ_(Ω)) CN(Φ_(Ω))Gaussian sensor ratio % BP accuracy % Insense 0.3165 ± 0.0023 9320 ±3292 1.46 ± 0.07 100 ± 0  58.55 ± 2.64 FrameSense 0.3273 ± 0.0059 6095 ±1708 3.19 ± 0.92 84 ± 5  58.15 ± 2.26 EigenMaps 0.3215 ± 0.0021 7230 ±2319 2.07 ± 0.12 90 ± 0  57.60 ± 3.72 MSE-G 0.5805 ± 0.0440 78530 ±12450 5.99 ± 0.31 17 ± 4  49.90 ± 3.54 MI-G 0.6814 ± 0.0556  93260 ±109250 6.26 ± 0.77 7 ± 4 51.60 ± 5.21 Entropy-G 0.7007 ± 0.0804 98950 ±16216 6.61 ± 0.48 5 ± 7 53.70 ± 5.21 Determinant-G 0.7303 ± 0.0545105700 ± 11228  6.57 ± 0.31 3 ± 4 55.50 ± 4.50 Greedy SS 0.7303 ± 0.0545105700 ± 11228  5.57 ± 0.31 3 ± 4 55.50 ± 4.50 Convex SS 0.5788 ± 0.114075270 ± 27383 5.97 ± 0.77 20 ± 15 54.40 ± 4.20

Microbial diagnostics. The performance of Insense was assessed on areal-world dataset from microbial diagnostics. Microbial diagnosticsseek to detect and identify microbial organisms in a sample. Nextgeneration systems detect and classify organisms using DNA probes thatbind (hybridize) to the target sequence and emit some kind of signal(e.g., fluorescence). Designing DNA probes for microbial diagnostics isan important application of sensor selection in the underdeterminedsensing regime. For example, in the universal microbial sensing (UMD)framework (Aghazadeh et al., 2016), DNA probes acquire linearmeasurements from a microbial sample (e.g., bacterial, viral, etc.) inthe form of a fluorescence resonance energy transfer (FRET) signal thatindicates the hybridization affinity of a particular DNA probe to theorganisms present in the sample. Given a matrix Φ that relates thehybridization affinities of DNA probes to microbial species, theobjective is to recover a sparse vector x comprising the concentrationsof the organisms in the sample from as few linear measurements aspossible.

Insense and the baseline sensor selection algorithms were run on a largesensing matrix comprising the hybridization affinity of D=100 random DNAprobes to N=42 bacterial species (as described in Aghazadeh et al.,2016). For each algorithm, after selecting M probes and constructing asensing matrix Φ_(Ω) with |Ω|=M, BP recovery was performed for multiplesparse vectors x with random support (corresponding to the presence of arandom subset of bacterial organisms). The same experiment was repeatedfor all

sparse vectors x with K={2, 3, 5} non-zero elements (i.e., bacteriapresent), and the average BP recovery performance on identifying thecomposition of the samples is reported in Table 4. (To report the BPrecovery for K=5 organisms, the inventors randomly generate 1000realizations of the sparse vector x with 5 active elements and averagethe BP recovery performance on selected samples.)

The DNA probes selected by Insense outperform all of the baselinealgorithms in identifying the bacterial organisms present. Specifically,Insense requires a smaller number of DNA probes than the other algorithmto achieve almost perfect detection performance (BP accuracy>99%),suggesting that Insense is the most cost-efficient algorithm to selectDNA probes for this application. Moreover, the performance gap betweenInsense and the other algorithms grows as the number of bacterialspecies present in the sample K increases, indicating that Insense hasbetter recovery performance in complex biological samples.

TABLE 4 Comparison of Insense against the baseline algorithms onselecting M DNA probes to identify pathogenic samples containing Kbacterial organisms. Insense selected the sets of DNA probes thatachieved the best pathogen identification performance. BP accuracy indetecting organisms % Number of organisms K = 2 K = 3 K = 5 Number ofprobes (M) 5 8 12 15 8 12 15 20 12 15 20 25 Insense 25.52 68.33 94.7899.65 26.46 71.74 93.95 99.53 16.78 51.95 92.71 99.10 FrameSense 27.7361.83 88.40 95.71 22.70 62.32 82.29 98.36 10.79 35.16 81.92 96.50EigenMaps 14.97 49.65 84.69 94.66 13.17 54.68 78.09 96.25 6.69 27.4772.13 95.30 MSE-G 27.26 60.79 91.53 97.91 22.01 67.16 89.15 98.40 14.6943.26 83.52 97.40 MI-G 26.22 59.98 89.68 96.40 20.96 65.69 84.10 97.3913.48 37.96 79.72 96.00 Entropy-G 27.96 61.25 91.53 98.61 21.51 66.3588.96 99.19 14.19 42.86 89.61 97.50 Determinant-G 14.85 46.75 82.1394.55 12.49 48.97 76.13 96.03 6.29 24.48 72.73 92.81 Greedy SS 25.5257.54 87.70 96.87 19.72 59.65 84.64 97.34 10.99 36.16 80.22 94.11 ConvexSS 15.89 53.36 87.94 98.94 14.29 57.58 87.59 98.89 7.69 38.46 83.5298.40 Random 25.57 61.53 88.79 96.66 22.37 62.29 86.15 97.72 12.79 38.8882.94 86.44

Example 4 Use of Toehold Probes in UMD

While the theory of universal probes itself may allow for lower limitsof detection than what were experimentally observed, the UMD system islargely limited by the low specificity of the MB probes. In order toincrease the sensitivity and/or limit of detection, toehold probes wereused in place of the MBs.

Toehold-probes were first developed to increase the specificity ofhybridization between a probe and a target (Zhang et al., 2012). When asingle-stranded probe is added to a target, hybridization can occur witha varying degree of energies, based upon the degree of mismatches ornucleotide content present in the hybridized strands. Toehold-probesutilize probes that are pre-hybridized to a semi-complementary“protector” strand, such that hybridization of a probe to a given targetstrand only occurs following displacement of the protector from theprobe. In this way, probe hybridization will only occur to targetstrands that are more complementary (or more energetically favorable) tothe probe than the protector strand. By using protected-probes, noisecan be significantly reduced, allowing for higher resolution detection.

An additional advantage of using toehold-probes is that they are immuneto changes in genomic GC content. One issue encountered with the UMD MBprobes was that bacterial genomes containing a higher degree of guanineand cytosine motifs tended to hybridize more readily to the MB probesthan genomes containing a lower percent of these nucleotides (due to thestronger chemical interactions observed between GC bases). This isproblematic because it confounds the accuracy of predicted hybridizationaffinity values, and minimizes the range over which species containing ahigh percent of GC bases can bind to the MB probes. Toehold-probesbypass this issue, as they ensure that probe hybridizations are notdependent solely on the binding energy to the target, but on therelative binding energy of the target strand compared to the bindingenergy of the protector strand, thus eliminating a dependence on GC.

Magnetic bead functionalization. The first application of the toeholdprobes utilized streptavidin coated magnetic beads. Biotinylatedprotector strands were first complexed to complementary probe strands,then conjugated to streptavidin coated magnetic beads. Each probe strandwas flanked by primer-specific regions on either end to allow forbinding of primers to and subsequent amplification of the probe viaquantitative PCR (qPCR) (FIG. 19). Target DNA was next introduced intosolution and allowed to displace the protector strands. Afterprobe-target hybridization, a magnet was used to pull protector strandsout of solution, leaving only probe-target complexes in the supernatant.Following extraction and analysis of this supernatant, concentration ofprobe-target complexes was quantified via qPCR using primers specificfor the probe strands. For initial studies, target strands were designedto be 50 base pairs long and perfectly complementary to the probe.

While the magnetic beads scheme offered improvements in LOD (from 1 μMto 10 pM) compared to the previous molecular beacon scheme, theresolution conferred by the bead capture method was limited, in that theobserved probe concentration only spanned 2 orders of magnitude for thesix orders of magnitude dynamic range observed in target concentrations.Further, the experimental LOD was higher than theoretical estimates,likely due to non-specific probe interactions, and lower than expectedexperimental capture efficiencies (the percent of biotinylatedprotectors bound to streptavidin-coated beads). To minimize thepreponderance of non-specific interactions between the probe strands,the tube surface, and the beads, various blocking reagents (PolyT,Tween, and BSA) were tested. Of the three reagents examined, only theaddition of PolyT conferred significant improvements in probebackground.

To improve capture efficiencies, various experimental controls—includingbead concentration, protector to probe ratios, hybridization time,buffer composition, and mixing method—were optimized; despiteimplementation of such modifications, however, experimental captureefficiency failed to exceed 97%. While seemingly high, a captureefficiency of 97% is non-ideal because it results in the production of asignificant amount (3%) of background probe signal from the residualfree-floating probe-protector complexes that have not been captured.This ultimately limits the achievable dynamic range to two orders ofmagnitude or less.

To address the issue of capture efficiency, three additional methodswere explored to reduce background: 1) beads recapture, 2)pre-functionalization, and 3) double biotinylation. The beads recapturescheme involved running the probe, protector, and target solutionthrough a second bead conjugation protocol to capture any residualprotector/probe complexes that were not captured the first time. Thismethod produced ˜0.5% improvements in capture efficiency. Theprefunctionalization method, on the other hand, required removal of thesupernatant following initial conjugation of probe-protector complexesto the streptavidin-coated beads, but prior to addition of target DNA.In this way, the residual free-floating probe-protector complexes thathad not been captured were manually removed from the background. Similarto beads recapture, this method also produced minor improvements incapture efficiency.

In the last scheme, protectors that had been complexed to two biotinmolecules on their 3′ were utilized, as opposed to the protectors thathad been previously used that had been conjugated to only one biotinmolecule on their 3′ terminus. Application of these double-biotinylatedprotectors failed to produce any marked improvements in captureefficiency, however. Ultimately, future experimentation appeared tosuggest streptavidin leaching from the magnetic beads surface as themain factor responsible for the 97-98% capture efficiency observed withthe beads scheme. Overall performance of the magnetic beads system ispresented in FIG. 20.

Neutravidin surface functionalization. In addition to the magnetic beadsmethod, the inventors also attempted to utilize neutravidin coated96-well plates as a capture platform for biotinylatedprotector/protector-probe complexes. Neutravidin is a syntheticallymodified version of streptavidin that is known to exhibit decreasedoff-target binding to biotin. Similar to the streptavidin-coated beads,the neutravidin plate serves as an anchor that pulls downprobe-protector complexes out of solution, while allowing probe-targetcomplexes to float freely in the supernatant. As with the magnetic beadfunctionalization scheme, the dynamic range and sensitivity of theneutravidin capture platform was elucidated via construction of a seriesof log curves using either 0.1 nM, 1 nM, or 10 nM of probe DNA, and 100fM to 1 uM of target DNA (with each order of magnitude in betweentested). Initial examination of the system's performance exhibited adynamic range spanning 5 orders of magnitude—from 100 pM to 1 uM—and asensitivity of 100 pM. The resolution of the neutravidin plate systemalso appeared to be slightly worse than that of the magnetic beadssystem, given the 1.5 orders of magnitude range observed in probeconcentration corresponding to the 5 orders of magnitude dynamic rangeexhibited in target concentration (FIG. 21).

Closer analysis of the dynamic range revealed a much lower than expectedmaximum probe concentration, however. This, coupled with otherexperimental data, led us hypothesize that the probe and target DNAmolecules were nonspecifically interacting with the surface of the well.To mitigate the occurrence of such unfavorable interactions, theinventors attempted to block the neutravidin surface using both poly-Tand BSA. Coating of the surface with poly-T strands, in combination witha brief detergent treatment using 1% SDS, resulted in reduction innon-specific interactions between the probe-target complexes and theneutravidin surface.

Overall, comparison of the LOD and dynamic range achieved vianeutravidin surface functionalization revealed reductions in bothsensitivity and dynamic range relative to magnetic beadfunctionalization.

Various optimizations for hybridization time and volume were pursued inan attempt to improve the neutravidin plate system's performance (eithervia improvements in sensitivity, resolution, or dynamic range); howeversuch optimization efforts introduced no significant changes to thesystem's overall performance. While an increased hybridization timeshould theoretically improve sensitivity and resolution, especiallygiven the slower kinetics observed with surfaces relative to homogenoussolutions, non-favorable reactions—such as the non-specific interactionsbetween the target/probe strands and the neutravidin surface—appeared tobecome more predominant with increased time. Of even greater concern,the neutravidin plates exhibited substantial leaching from the platesurface over the longest incubation period (6 hours), resulting in an˜20% loss in capture efficiency.

RNAse H induced cleavage of probe-protector complexes. Rather than usinga capture-based scheme to isolate probe-protector complexes fromprobe-target complexes, restriction endonucleases were used to directlycleave probe-protector complexes. RNA bases were introduced into thenon-homologous region of the probe, in order to produce probeprotectorcomplexes containing regions of DNA bound to both RNA and DNA, andprobe-target complexes containing regions of DNA only bound to DNA (FIG.22). Aside from the introduction of these RNA bases, the probe size andsequence remained the same as that of the probes used in the magneticbead functionalization and neutravidin surface functionalizationschemes.

As with the magnetic bead functionalization and neutravidin surfacefunctionalization schemes probe-protector complexes were firstpre-annealed in solution. Varying concentrations of target strand(ranging from 10 fM to 1 uM) were then added to the solution, andallowed to displace the protector and hybridize to the probe. RNAse H2,a recombinant endo-ribonuclease that binds to RNA-DNA duplexes andcleaves 5′ to the RNA base was then added into the mix and allowed toincubate for 1 hour. Following heat inactivation of the RNAse, the totalsolution was extracted and analyzed via qPCR, using primers specific forthe probe. It should be mentioned that cleavage of the probe by RNAse H2removes only one of the probe-flanking regions that is complimentary tothe primer (i.e. the region specific for the forward primer); thus,cleavage products undergo slower, arithmetic amplification as opposed tothe faster, geometric amplification experienced by probes containingregions specific for both primers. Various incubation times and enzymeconcentrations were tested to determine optimal conditions for maximumprobe/protector cleavage (FIG. 23). Analysis of the results of the RNAseH induced cleavage of probe-protector complexes scheme revealed asensitivity of 10 pM and a dynamic range spanning six orders ofmagnitude (10 pM to 1 uM).

ScaI-HF induced cleavage of probe-protector complexes. Here again,restriction endonucleases were used for the purposes of cleavingprobe-protector complexes in solution. In this scheme, a ScaI-HFrestriction site was introduced into the non-homologous region of theboth the probe and protector in order to induce cleavage ofprobe-protector complexes, while leaving probe-target complexes intact(FIG. 24). The protocol applied in this scheme followed exactly from theRNAse H induced cleavage of probe-protector complexes scheme. Thedynamic range achieved with the Sca1-HF method spanned approximately 5orders of magnitude; the LOD of the system was approximately 100 pM. Theresolution for ScaI-HF induced cleavage of probe-protector complexesappeared to be greater than the resolutions seen in the magnetic beadfunctionalization, neutravidin surface functionalization, and RNAse Hinduced cleavage of probe-protector complexes schemes, given the 2orders of magnitude range in probe concentrations observed for the 5orders of magnitude dynamic range in target concentration (FIG. 25).

As with the RNAse H induced cleavage of probe-protector complexes,various incubation times were tested to determine optimal conditions forcleavage using ScaI-HF (FIG. 26). While 88% cleavage was achieved with1-hour incubation with the enzyme, 98% cleavage was observed with a24-hour incubation period. However, slight probe/target cleavage wasalso detected in the latter condition. These results suggest thatimprovements in the ScaI-HF induced cleavage of probe-protectorcomplexes scheme's performance may be achieved with increased incubationtimes, and perhaps optimization of other incubation parameters (such asenzyme concentration).

Size exclusion via column chromatography. The last scheme involved usingsize exclusion chromatography to preferentially capture bacteria andbound probes, while allowing unbound probes to become discarded (viacolumn filtration). Given the relative sizes of the bacterial genomesand probes used, 100 kDA columns were specifically chosen for thisapplication. Unlike the previous schemes, this scheme utilized actualbacterial genomes (from clinical isolates of MRSA) as the target DNA.First, varying concentrations of probe—10 nM, 1 nM, and 0.1 nM—weremixed with varying concentrations of bacterial DNA. Following a 2-hourincubation, the bacterial DNA-probe mixture was pipetted into the top ofthe size filtration column. An additional 30-minute incubation periodwas observed to allow unbound probe to flow through the column filter.After three rounds of centrifugation and washing, the bacterial genomeand bound probes were extracted, diluted, and analyzed via qPCR.Examination of the size exclusion via column chromatography scheme'sresults demonstrated poor sensitivity, a limited dynamic range, and alower resolution compared to the previous schemes (FIG. 27).Additionally the column filtration method appeared to produce extremeinconsistencies in probe concentrations, and facilitate poor clearanceof unbound probe. Based on subsequent experimentation, it appeared thatthe columns were becoming clogged—either by the excess probe-protectorcomplexes or the bacterial genomes—thereby preventing filtration ofunbound probe.

Example 5 smFISH for Detection of Probe Binding to Bacterial Genomes

Single molecule fluorescent in situ hybridization (smFISH) is anothermethod of detecting the differential binding of probes to bacterialgenomes that has the potential to improve the resolution and processingspeeds for the identification of bacterial species. The inventorspropose utilizing single molecule Fluorescence in situ Hybridization(smFISH) in combination with universal probes as a method of clinicalmicrobial identification. FISH is a well-established system fordetecting DNA or RNA in situ using fluorescent probes and (in mostmodern applications) digital imaging. Previous studies have validatedthe ability of FISH to resolve binding of just a single probe to itscomplementary target with high accuracy via methods that arecumulatively known as single molecule FISH (smFISH) (Femino et al.,1998; Raj et al., 2008). Using these techniques, the extent of universalprobe hybridization to the bacterial genome can be accurately quantifiedvia measurement of fluorescence intensity.

SmFISH bears numerous advantages over the previously described methodsfor a number of reasons. First, given that the technique isfundamentally an in situ method, probes can be delivered directly intocells, eliminating the need for DNA extraction and amplification.Further, as an imaging platform, this method precludes the need forphysical separation of bacterial cells from human cells, as the two caneasily be distinguished through visual discrimination. Second, smFISHobviates the necessity for the complicated de-convolution post-analysescarried out with the UMD and other preliminary schemes. This isprimarily because the bacterial concentration is known (based on thenumber of cells imaged), therefore reducing the number of unknownvariables to one—bacterial identity. Third, smFISH maintainssignificantly higher resolution and sensitivity compared to the previoustechnologies explored; as stated previously, some studies have evenreported the ability to image single mRNA molecules using smFISH (Feminoet al., 1998). Thus, this method should theoretically be able to resolveclinically relevant concentrations of bacteria.

One difficulty often encountered when working with smFISH strategies isthe limited number of optically unique probes that can be deliveredsimultaneously. As shown with the previous UMD data, using a greaternumber of universal probes can often to lead to improvements in bothspecificity and sensitivity. Thus, a limitation on the number of probesthat can be delivered simultaneously may be potentially cumbersome tothe proposed strategy, as it imposes an upper limit on the diagnosticperformance of the system. One way to circumvent this issue is bydelivering probes iteratively, rather than simultaneously. However, thisstrategy would be too timeintensive, and ultimately in conflict with thegoal of expediting microbial identification.

An alternative strategy that can be employed is spectral barcoding.Spectral barcoding is a method that identifies oligonucleotide sequencesbased on the distinctive combination of fluorescent signals that arisefrom the hybridization of differentially labeled fluorescent probes tothat sequence (Itzkovitz & van Oudenaarden, 2011). Rather thanconjugating a single spectrally unique fluorophore to each probe in thesystem, each probe can be multiplexed with a combination of n suchfluorophores to produce 2^(n)−1 probes with unique fluorescent/colorcombinations, therefore providing us with greater flexibility in probedesign (Itzkovitz & van Oudenaarden, 2011). Using spectral barcodingtechniques, the library of available unique probes can be vastlyexpanded. Thus, this strategy allows for the fulfillment of rapidity,while simultaneously expanding the achievable diagnostic performance ofthe smFISH system.

To use smFISH for detection, candidate probe sequences are selected, andthe chosen probes are synthesized as Molecular Beacons (containing a 38bp long loop region and 4 bp long stem regions) and conjugated to a Cy3and Cy5 fluorophore on either end. The constructed MB probes will thenbe delivered into individual aliquots of cultured and fixed B. subtiliscells—each with varying concentration—using the smFISH protocoldescribed below, and imaged under fluorescence. The concentration ofcells used will vary between 1 uM (the cell concentration utilized inall previous UMD experiments), and 10 pM, with each bacterial cellaliquot differing in concentration by one order of magnitude. Theconcentration of probes delivered per condition will always be 10× theconcentration of bacterial cells in that aliquot, to ensure that probesare in excess of the bacterial genome. For future applications, in whichthe starting bacterial concentration is not known, probes will be addedin decreasing log dilutions to individual aliquots of the bacterialsample, such that the ratio of probe to bacterial genomic DNAremains >1, but not so high as to produce significant background.

To perform smFISH, bacteria are cultured overnight and then 1 mL ofbacteria is pipetted into eppendorf tubes, to each of which 0.5 mL 1×PBS is added. The tubes are centrifuged at 3000 g for 5 minutes. Whilecentrifuging, the probe/protector duplexes are prepared as follows. Fora 100 uL, 10 uM solution, 10 uL of 100 uM biotinylated probe and 25 uLof 100 uM protector (2.5× excess) are combined with 65 uL sterile waterand allowed to incubate at room temperature for ˜3 hrs. Alternatively,annealing can be performed using a thermocycler annealing protocol. Oncethe bacterial tubes complete centrifugation, the supernatant isdiscarded and the pellet is resuspended in 0.5 mL 1× PBS. Then the tubesare centrifuged at 3000 g for 3 minutes. Supernatant is again discardedand 1.0 mL of ice cold BD cytofix is added to each tube. The tubes areincubated on a nutator at room temperature for 30 minutes. Afterincubating, centrifuge for 3.5 minutes at 3000 g, and wash twice with 1×PBS by discarding the supernatant, adding 1.0 mL 1× PBS, centrifuging at6000 g for 3.5 minutes, and repeating. Then, 350 uL Lysis buffer (10 mMTris HCL, pH 8.0/8.3, 0.1 M NaCl, 1 mM EDTA, 55 and 5% w/v Triton X-100;for a 10 mL solution, this involves 100 uL Tris-HCL, pH 8.3+0.058 gNaCl+20 uL 0.5 M EDTA, pH 8.0+500 uL Triton X-100) is added to each tubefollowing by 100 uL lysozyme solution. Incubate at 32 degrees insemi-sterile incubator, on nutator, for 30 minutes, and then wash twicewith 1× PBS. Add 100 uL RNAse A solution to each tube, and incubate at60 degrees C. (in shake rack incubator) for 1.5 hrs at 250 rpm. Washtwice with 1× PBS; resuspend pellet in 1 mL 60% formamide (w/v in 2×SSC)and incubate at 65 degrees C. for 5 minutes. Centrifuge samples for 3-5minutes at 3000×g. Add 45 uL hybridization buffer (1× PBS+60 mg/mL BSA(6%))and 5 uL 40 nM probe to each tube and resuspend pellet. Incubatetubes in semi-sterile incubator for 4-16 hours. Collect supernatant frombacterial/probe tubes and store in separate Eppendorf tubes. Resuspendpellets in .5 mL 1× PBS and wash once. Stain using Hoechst Blue—5 min.Wash with 1× PBS twice—place on slide and image.

Based on the average total fluorescent intensity outputs of eachcondition, a calibration curve shall be constructed to ascertain thecorrelation between experimentally observed and theoretically obtainedhybridization profiles. Any disagreement (error>10%) observed inexperimental binding profiles will necessitate modification andoptimization of the simulation parameters used. These parameters mayinvolve varying the GC content of the generated probes, optimizingnearest-neighbor algorithms, or altering dangle energy contributions,for example.

In regards to the smFISH platform described here, sensitivity can bedefined in two of the following ways: either 1) the minimum number ofprobes that can be detected, or 2) the minimum number of bacterial cellsthat can be successfully identified. For the purposes of clarity, theformer shall be referred to as the detection limit, or LOD, while thelatter shall be addressed as diagnostic sensitivity.

As stated previously, based on data from previous smFISH studies, theproposed system should be theoretically capable of detecting at least aminimum of 48 probe bindings with high accuracy (Raj et al., 2008).However, probes that exhibit low bindings to B. subtilis (1, 5, 10, 25,and 50 bindings respectively) shall also be separately tested toelucidate the detection limit of the smFISH protocol. Various washbuffers and blocking agents (such as BSA or Poly-Lysine) will be testedto minimize non-specific probe binding. Lastly, the baselineconcentration of cells needed to resolve at the limit of detection (LOD)shall be probed via calculation of the mean fluorescent intensities and% error using a varying range of cell concentrations (10 pM to 1 uM), inorder to gage the diagnostic sensitivity of the platform.

Having established the LOD and diagnostic sensitivity of the MB probes,use of sloppy toe-hold probes will be attempted to further improvesensitivity. As stated previously, toe-hold probes were initiallydeveloped by Dr. Dave Zhang as a method to improve the specificity ofhybridization to targets containing SNPs (Zhang et al., 2012). However,the efficacy of these probes in the presence of mismatch heavy(“sloppy”) protectors has never been validated. “Sloppy protectors” aredesirable for the specific application, in that they allow for morecustomizable fine-tuning of probehybridization profiles. As statedpreviously, the application of a protector serves to restrict theallowable range of delta G's that can be observed in binding. In thecase of sloppy protectors, the aforementioned range can be modulateddepending on the delta G of binding between the probe and protector,which can in turn be controlled by varying the number of mismatches inthe protector. Thus, the inventors shall seek to validate the efficacyof sloppily protected probes against various energy targets.

Utilizing the X-probe design parameters described in (Wang & Zhang,2015), five sets of Xprobes shall be created using protectors thatexhibit 1-5 mismatches in their sequence, relative to the complementprobe strand. Each of these X-probes will be tested against targetscontaining 0-4 mismatches. Displacement of each of the Xprobes in thepresence of each target will be determined both in silico (via NuPACK)and experimentally (Zadeh et al., 2011). If the sloppy X-probes functionin agreement with theoretical predictions, thereby confirming theefficacy of “mismatch” heavy protectors, the inventors shall utilize thesame sequence tested above to create a toehold probe that binds at thedetection limit to the B. subtilis genome, and re-examine the errorprofiles (in terms of total fluorescent intensity) for the sameconcentrations of cells to evaluate potential improvements in noise.

The “low-binding” toe-hold probe shall also be delivered into individualaliquots of B. subtilis cells that exhibit concentrations rangingbetween 1 uM and 10 pM, with each aliquot differing in concentration byone order of magnitude, to evaluate improvements in diagnosticsensitivity. As with the previous experiments described in Example 5,the toe-hold probes will be delivered at a 10-fold concentrationrelative to bacterial cells.

Using the sloppy toehold probes, the inventors expect to seeimprovements in probe-binding specificity when compared to unprotectedprobes or MB probes. If no such agreement is observed however, theutilization of unprotected probes or MB probes in combination withsmFISH should still achieve a higher LOD than the probes used in theinitial UMD design, given smFISH's ability to glean quantitativefluorescence data from individual cells rather than whole cellpopulations. This capability should ultimately serve to improvesensitivity beyond what was achieved using the previous UMD system.

All of the compositions and methods disclosed and claimed herein can bemade and executed without undue experimentation in light of the presentdisclosure. While the compositions and methods of this disclosure havebeen described in terms of preferred embodiments, it will be apparent tothose of skill in the art that variations may be applied to thecompositions and methods and in the steps or in the sequence of steps ofthe method described herein without departing from the concept, spiritand scope of the disclosure. More specifically, it will be apparent thatcertain agents which are both chemically and physiologically related maybe substituted for the agents described herein while the same or similarresults would be achieved. All such similar substitutes andmodifications apparent to those skilled in the art are deemed to bewithin the spirit, scope and concept of the disclosure as defined by theappended claims.

VI. REFERENCES

The following references, to the extent that they provide exemplaryprocedural or other details supplementary to those set forth herein, arespecifically incorporated herein by reference.

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1. A method of detecting a bacterial infection in a subject comprising:(a) providing a set of probes comprising SEQ ID NOS: 1, 2, 3, 4 and 5,and optionally having SEQ ID NOS: 6, 7, 8, 9 and 10, respectively,hybridized thereto; (b) providing a first sample from said subject; (c)obtaining hybridization information for each of probes SEQ ID NOS: 1, 2,3, 4 and 5 with one or more bacterial genomes in said sample; and (d)identifying the presence of one or more bacterial genomes in said samplebased on a predetermined hybridization pattern for said set of probeswith a given bacterial genome.
 2. The method of claim 1, wherein saidfirst sample is a body fluid.
 3. (canceled)
 4. The method of claim 1,wherein detection comprises detecting more than one bacterial genome. 5.The method of claim 4, wherein the more than one bacterial genomes arefrom the same species.
 6. The method of claim 4, wherein the more thanone bacterial genomes are from different species.
 7. (canceled)
 8. Themethod of claim 1, wherein said each of said probes are comprised withina molecular beacon.
 9. (canceled)
 10. The method of claim 1, whereineach of said probes carries a label. 11-12. (canceled)
 13. The method ofclaim 1, wherein said or bacterium or bacterial genomes are frompathogenic bacteria. 14-18. (canceled)
 19. The method of claim 1,wherein step (c) comprises obtaining quantitative hybridizationinformation.
 20. The method of claim 19, further comprising quantitatingthe number of bacterial genomes in said sample.
 21. The method of claim20, further comprising performing steps (a)-(d) on a second sample fromsaid subject.
 22. The method of claim 21, wherein said second sample wasobtained at a second point in time as compared to said first sample, andthe number of bacterial genomes in said first and second samples iscompared.
 23. The method of claim 22, wherein an anti-bacterial therapywas administered between obtaining of said first and second samples, andsaid method assesses therapeutic efficacy.
 24. The method of claim 1,further comprising treating said subject for a bacterial infection. 25.The method of claim 1, wherein said subject is a human or non-humanmammal.
 26. The method of claim 1, further comprising classifying thebacterial infection.
 27. The method of claim 1, further comprisingtreating said subject based on the identification of one or morebacterial genomes in step (d).
 28. A kit comprising a set of probescomprising SEQ ID NOS: 1, 2, 3, 4 and 5, and optionally having SEQ IDNOS: 6, 7, 8, 9 and 10, respectively, hybridized thereto. 29-42.(canceled)
 43. A method of detecting a bacterial infection in a subjectcomprising: (a) providing a set of M random DNA probes selected by: (i)generating a sensing matrix comprising the hybridization affinity of Drandom DNA probes to N bacterial species; and (ii) determining the setof M random DNA probes having a smallest average coherence among thebacterial species; (b) providing a first sample from said subject; (c)obtaining hybridization information for each of the M random DNA probeswith one or more bacterial genomes in said sample; and (d) identifyingthe presence of one or more bacterial genomes in said sample based onsaid hybridization information. 44-71. (canceled)
 72. A kit comprising aset of probes comprising M random DNA probes selected by: (a) generatinga sensing matrix comprising the hybridization affinity of D random DNAprobes to N bacterial species; and (b) determining the set of M randomDNA probes having a smallest average coherence among the bacterialspecies. 73-80. (canceled)